environment and economic growth essay

Environmental impact of economic growth

Economic growth means an increase in real output (real GDP). Therefore, with increased output and consumption we are likely to see costs imposed on the environment. The environmental impact of economic growth includes the increased consumption of non-renewable resources, higher levels of pollution, global warming and the potential loss of environmental habitats.

However, not all forms of economic growth cause damage to the environment. With rising real incomes, individuals have a greater ability to devote resources to protecting the environment and mitigate the harmful effects of pollution. Also, economic growth caused by improved technology can enable higher output with less pollution.

Classic trade-off between economic growth and environmental resources

This PPF curve shows a trade-off between non-renewable resources and consumption. As we increase consumption, the opportunity cost implies a lower stock of non-renewable resources.

For example, the pace of global economic growth in the past century has led to a decline in the availability of natural resources such as forests (cut down for agriculture/demand for wood)

• A decline in sources of oil/coal/gas
• Loss of fishing stocks – due to overfishing
• Loss of species diversity – damage to natural resources has led to species extinction.

External costs of economic growth

• Pollution. Increased consumption of fossil fuels can lead to immediate problems such as poor air quality and soot, (London smogs of the 1950s). Some of the worst problems of burning fossil fuels have been mitigated by Clean Air Acts – which limit the burning of coal in city centres. Showing that economic growth can be consistent with reducing a certain type of pollution.
• Less visible more diffuse pollution. While smogs were a very clear and obvious danger, the effects of increased CO2 emissions are less immediately obvious and therefore there is less incentive for policymakers to tackle. Scientists state the accumulation of CO2 emissions have contributed to global warming and more volatile weather. All this suggests economic growth is increasing long-term environmental costs – not just for the present moment, but future generations.
• This graph shows CO2 emissions per capita. It shows a 66% rise in per capita pollution between 1960 and 2014. The total emissions are also higher because of population growth. 1960 to 2014 was a period of strong economic growth and despite the development of new technologies, has failed to halt the rise. The last few years 2011 to 2014 show a levelling – this is only a short time range, but could be due to improved global efforts to reduce pollution. (it was also a period of low economic growth in Western economies)
• Damage to nature . Air/land/water pollution causes health problems and can damage the productivity of land and seas.
• Global warming and volatile weather . Global warming leads to rising sea levels, volatile weather patterns and could cause significant economic costs
• Soil erosion . Deforestation resulting from economic development damages soil and makes areas more prone to drought.
• Loss of biodiversity. Economic growth leads to resource depletion and loss of biodiversity. This could harm future ‘carrying capacity of ecological systems’ for the economy. Though there is uncertainty about the extent of this cost as the benefit of lost genetic maps may never be known.
• Long-term toxins . Economic growth creates long-term waste and toxins, which may have unknown consequences. For example, economic growth has led to increased use of plastic, which when disposed of do not degrade. So there is an ever-increasing stock of plastic in the seas and environment – which is both unsightly but also damaging to wildlife.

U-Shaped curve for economic growth and the environment

One theory of economic growth and the environment is that up to a certain point economic growth worsens the environment, but after that the move to a post-industrial economy – it leads to a better environment.

For example – since 1980, the UK and the US have reduced CO2 emission. The global growth in emissions is coming from developing economies.

Another example – In early days of growth, economies tend to burn coal/wood – which cause obvious pollution. But, with higher incomes, an economy can promote cleaner technology which limits this air pollution. However, in a paper “ Economic growth and carrying capacity ” by Kenneth Arrow et al. they caution about this simplistic u-shape. As the authors state:

“Where the environmental costs of economic activity are home by the poor, by future generation, or by other countries, the incentives to correct the problem are likely to be weak”

• It may be true there is a Kuznets curve for some types of visible pollutants, but it is less true of more diffuse and less visible pollutants. (like CO2)
• The U-shaped maybe true of pollutants, but not the stock of natural resources; economic growth does not reverse the trend to consume and reduce the quantity of non-renewable resources.
• Reducing pollution in one country may lead to the outsourcing of pollution to another, e.g. we import coal from developing economies, effectively exporting our rubbish for recycling and disposal elsewhere.
• Environmental policies tend to deal with pressing issues at hand but ignore future intergenerational problems.

Other models of a link between economic growth and environment

Limits Theory

This suggests that economic growth will damage the environment, and damage will itself start to act as a brake on growth and will force economies to deal with economic damage. In other words, the environment will force us to look after it. For example, if we run down natural resources, their price will rise and this will create an incentive to find alternatives.

This is more pessimistic suggesting that economic growth leads to an ever-increasing range of toxic output and problems, some issues may get solved, but they are outweighed by newer and more pressing problems which are difficult if impossible to overturn.

This model has no faith that the free-market will solve the problem because there is no ownership of air quality and many of the effects are piling up on future generations; these future effects cannot be dealt with by the current price mechanism.

Race to the bottom

This suggests that in the early stages of economic growth, there is little concern about the environment and often countries undermined environmental standards to gain a competitive advantage – the incentive to free-ride on others’ efforts. However, as the environment increasingly worsens, it will reluctantly force economies to reduce the worst effects of environmental damage. This will slow down environmental degradation but not reverse past trends.

Economic growth without environmental damage

Some ecologists argue economic growth invariably leads to environmental damage. However, there are economists who argue that economic growth can be consistent with a stable environment and even improvement in the environmental impact. This will involve

• A shift from non-renewables to renewables A recent report suggests that renewable energy is becoming cheaper than more damaging forms of energy production such as burning coal and in 2018 – this has led to a 39% drop in new construction starts from 2017, and an 84% drop since 2015.
• Social cost pricing. If economic growth causes external costs, economists state it is socially efficient to include the external cost in the price (e.g. carbon tax ). If the tax equals the full external cost, it will lead to a socially efficient outcome and create a strong incentive to promote growth that minimises external costs.
• Treat the environment as a public good . Environmental policy which protects the environment, through regulations, government ownership and limits on external costs can, in theory, enable economic growth to be based on protection of the environmental resource.
• Technological development . It is possible to replace cars running on petrol with cars running on electricity from renewable sources. This enables an increase in output, but also a reduction in the environmental impact. There are numerous possible technological developments which can enable greater efficiency, lower costs and less environmental damage.
• Include quality of life and environmental indicators in economic statistics. Rather than targetting GDP, environmental economists argue we should target a wider range of living standards + living standards + environmental indicators. (e.g. Genuine Progress Indicators GPI )

Source: Ida Kubiszewski et al, “Beyond GDP: Measuring and Achieving Global Genuine Progress,” Ecological Economics, 93, (2013).

• Environmental sustainability

15 thoughts on “Environmental impact of economic growth”

It’s really interesting and useful to me, but I need to know more about the relationship between economic growth, energy consumption and environmental degradation

Verry understandable

GDP is direct related with Energy generation, Carbon dioxide Emission (due to fossil fuel use in electric & motive power generation },Resource Depletion/Degradation, Global Warming and Climate Change. Reports by various agencies including DOE, IPCC, World Bank etc are sources for these information. I have covered this subject in my latest book entitled ”Green Tribology, Green Surface Engineering and Global Warming,ASM International, OH, USA, 2014” with hundreds of references for further studies.Ram, Austin, Texas,

The imminent collapse of ecosystems can only be stopped by economic diminution, dispensing with all unnecessary products and reducing the total production to the most essential. The concept of degrowth should be implemented as fast as possible on a larger scale. Eco-sufficiency and life quality are more important than profit maximization. https://degrowth.org/ https://www.degrowth.info/en/ https://en.wikipedia.org/wiki/Degrowth

Life quality as mentioned above, in my sense, is a little bit vague term. Life quality now is better than the ice-age we had. Environmental protection versus at the cost of economic supportive growth will bring to some good extent non-collapsible ecosystem home.

this essay give me some importance information and it really helpful to me. also, l wanna know how economic growth impact natural environment?

Very useful.

True it has really helped me in my studies. Thank you.

This just helped me with my exam. Thank God!

The size of our planet is finite. Continued growth will eventually result in saturation, starvation etc. 2bn people to the current 8bn in 100 years, and governments all still say growth is a good thing? It might end humans quicker (good for the planet, which has plenty if time to recover) but not so good for us.

Very informative stuff, especially on the environment, quite an eye opener for me and truly intrigued.

I FIND YOUR MATERIALS TO BE VERY EDUCATIVE, KINDLY INCLUDE ME ON YOUR MAILING LIST. REGARDS

i found its very educative & knowledgeable. especially the curve system for environment affect to the gdp. its an eye opener information to the respective researches. informative aspects clear.

I have been suffering with course work but thank you very much

Is the graph at 2.2a (Limits Theory) possible or meaningful? It suggests that beyond a certain level of GDP per capita there are two possible amounts of environmental damage.

Take your pick?

Where next (arrow)?

I suggest this ‘graph’ has not been thought through, and is meaningless.

Comments are closed.

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Economic Growth and the Environment

In this section, cid working paper no. 56, environment and development paper no. 4.

Theodore Panayotou

Will the world be able to sustain economic growth indefinitely without running into resource constraints or despoiling the environment beyond repair? What is the relationship between steadily increasing incomes and environmental quality? This paper builds on the author's earlier work (1993), in which he argued that the relationship between economic growth and environmental quality – whether inverse or direct -- is not fixed along a country's development path. Indeed, he hypothesized, it may change as a country reaches a level of income at which people can demand and afford a more efficient infrastructure and a cleaner environment. This implied inverted-U relationship between environmental degradation and economic growth came to be known as the "Environmental Kuznets Curve," by analogy with the income-inequality relationship postulated by Kuznets (1965, 1966).

The objective of this paper is to critically review, synthesize and interpret the literature on the relationship between economic growth and environment. This literature has followed two distinct but related strands of research: an empirical strand of ad hoc specifications and estimations of a reduced form equation, relating an environmental impact indicator to income per capita; and a theoretical strand of macroeconomic models of interaction between environmental degradation and economic growth, including optimal growth, endogenous growth and overlapping generations models. The author concludes that the macroeconomic models generally support the empirical findings of the Environmental Kuznets Curve literature. He suggests further empirical investigation related to the assumption of additive separability, as well as development of additional macroeconomic models that allow for a more realistic role for government.

Keywords: economic growth, environment, Kuznets Curve

JEL codes: O11, O13

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• Published: 17 April 2024

The economic commitment of climate change

• Maximilian Kotz   ORCID: orcid.org/0000-0003-2564-5043 1 , 2 ,
• Anders Levermann   ORCID: orcid.org/0000-0003-4432-4704 1 , 2 &
• Leonie Wenz   ORCID: orcid.org/0000-0002-8500-1568 1 , 3  

Nature volume  628 ,  pages 551–557 ( 2024 ) Cite this article

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• Environmental economics
• Environmental health
• Interdisciplinary studies
• Projection and prediction

Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

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Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References  7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs.  2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs.  2 , 18 , we use climate variables in their first-differenced form following ref.  3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs.  2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref.  19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section  1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section  2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section  2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section  3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref.  5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref.  2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs.  3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref.  2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref.  2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref.  35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section  5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs.  7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref.  15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs.  57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs.  7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section  2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, \({\bar{T}}_{x,y}\) , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, \({\widetilde{T}}_{x,y}\) :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , \({\bar{T}}_{x,m,{y}}\) is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref.  8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref.  18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L  > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs.  2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, \({\hat{T}}_{r}\) , or annual mean temperature, \({\bar{T}}_{r}\) , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref.  61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N  =  M  = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs.  2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section  1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

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Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

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Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref.  2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

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Economic Growth and the Environment

Using data assembled by the Global Environmental Monitoring System we examine the reduced-form relationship between various environmental indicators and the level of a country's per capita income. Our study covers four types of indicators: concentrations of urban air pollution; measures of the state of the oxygen regime in river basins; concentrations of fecal contaminants in river basins; and concentrations of heavy metals in river basins. We find no evidence that environmental quality deteriorates steadily with economic growth. Rather, for most indicators, economic growth brings an initial phase of deterioration followed by a subsequent phase of improvement. The turning points for the different pollutants vary, but in most cases they come before a country reaches a per capita income of $8,000.

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Essay On Economic Growth and Environmental Quality

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The major premise of this essay is that production-consumption and waste emis-sions tend to be joint products of the human species. Given this premise, a parable of an astronaut irretrievably lost in space is discussed in order to deduce several propositions on the astronaut’s optimal rate of consumption. A Harrod type of model is also analyzed with regard to the rate of consumption over time where the model includes a simplified depiction of the interaction between the economy and natural environment. Empirical estimates of the impact of effluent charges on comparative international advantage of selected countries are also presented. The major conclusion is that national and international economic policies and national environmental policies are not separable.

A portion of the research reported on here was financially supported by Resources For The Future, Inc., Washington, D.C. with no responsibility for the inferences or results contained herein.

I wish to acknowledge the comments and criticisms of the following individuals without committing them to agreement with any part of this essay: K. C. Kogiku, T. Crocker, K. Oddson, E. Brook, O. Bubik, T. Clark, and H. Lawton.

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Original research article, environmental protection or economic growth the effects of preferences for individual freedoms.

• Czech University of Life Sciences Prague, Faculty of Economics and Management, Prague, Czechia

Environmental protection is often seen in conflict with individual freedom and economic growth. The proponents of environmental protection suggest that the environment is a global resource that must be protected for future generations, even at the expense of economic growth and individual freedoms. The opponents claim that environmental protection should not come at the expense of individual rights and liberties, economic growth included. This paper studies the associations between public preferences for environmental protection, economic growth, and individual freedoms in eleven post-soviet countries on a representative dataset ( N = 20006, age 18+, M ± SD: 46,04 ± 17,07; 58% women, 46,8% upper education). Methodologically we rely on correlations, principal component analysis, and ordinal regression analyses. The results suggest that preferences for most personal freedoms studied predict environmental protection and economic growth preferences. In addition, preferences for civil rights, rights for democracy, gender equality, income inequality, and the low role of the army in politics predicted higher preferences for environmental protection and economic growth. Interestingly, the government’s right to video surveillance in public areas, though diminishing personal freedoms in terms of anonymity, predicted higher preferences for environmental protection and economic growth. The importance of God in lives proved to increase preferences for environmental protection but was negatively related to preferences for economic growth. We suggest the government communicate the need for environmental protection as a part of the rights for individual freedom to live in a clean environment.

1 Introduction

Increasing environmental degradation has received considerable attention from policymakers and academic communities ( Sinha et al., 2020 ; Cheng et al., 2021 ). Governments spelled out mitigation strategies for addressing the challenges of climate change in Intended Nationally Determined Contributions (INDCs) adopted in the Paris Agreement. The European Union and national governments have set clear objectives of where to be by 2050, under the EU priorities and Green Deal policies and with the support of dedicated research programs, legislation, and funding. Numerous environmental regulations around the globe abound.

The literature indicates that the relationship between economic growth and environmental quality is U-shaped (Environmental Kuznets Curve, EKC). While economic performance in poorer countries leads to a decrease in environmental quality, the association is reversed in richer countries ( Shahbaz et al., 2013 ; Stern, 2017 ; Anwar et al., 2022 ). Research shows that Post Soviet Union countries have not yet achieved the turning point ( Yang et al., 2017 ; Hasanov et al., 2019 ; Hasanov et al., 2023 ), meaning the tradeoff between economic growth and environmental quality is very apparent and calls for the implementation of environmental regulations.

Environmental regulations may reduce immediate economic performance by imposing additional costs and risks ( Nikolaou et al., 2014 ; Demertzidis et al., 2015 ; Hashmi and Alam, 2019 ). Environmental regulations also motivate firms to adopt new technologies, which may increase economic growth in the long run ( Sarkodie et al., 2019 ; Fan and Hao, 2020 ; Dechezleprêtre, et al., 2022 ). Less developed countries are shown to be less willing to invest in long-term environmental protection at the expense of immediate satisfaction of their material needs (the poverty-induced environmental degradation, Masron and Subramaniam, 2019 ; Moseley, 2001 ). In fact, poverty is shown among the principal sources of environmental damage across the countries ( Masron and Subramaniam, 2019 ). Thus, the tradeoff between economic performance and environmental protection is essential, especially in less abundant countries ( Sarkodie and Strezov, 2019 ; Güngör et al., 2021 ; Al-Mulali et al., 2022 ).

Besides economic performance, environmental regulations inevitably affect individual freedoms, including the freedoms of democracy and the corresponding role of the government. Economic and political freedoms indicate systemic differences across countries and are shown to significantly affect environmental degradation, as well as the preferences and costs of environmental protection ( Zhang et al., 2019 ; Bruun, 2020 ; Halvorson, 2021 ; Anwar et al., 2022 ). However, preferences for political and economic freedoms are rarely considered in predicting preferences for environmental protection ( Joshi and Beck, 2018 ).

This paper aims to study the role of the preferences for individual freedoms and the role of the government in predicting preferences for environmental protection and economic growth in Post-Soviet countries. Since many of the Post-Soviet countries are highly religious, we also hypothesize that religiosity contributes to the preferences for environmental protection (similar to Eom, et al., 2021a ). The following hypotheses are tested:

• H1. Preferences for individual freedoms predict preference for environmental protection.

• H2. The preferred role of the government predicts preferences for environmental protection.

• H3. Religiosity affects the preference for environmental protection.

We rely on a representative survey-based dataset from eleven Post-Soviet countries (N = 20006, age 18+, M ± SD: 46,04 ± 17,07; 58% women, 46,8% upper education). As economic performance is of immense importance in less-affluent post-soviet countries, we also test a similar set of hypotheses to predict the preferences for economic growth as one of the country’s priorities. This enabled us to contrast factors predicting environmental protection to factors predicting preference for economic growth at the expense of other social goals, such as military spending or making the cities and countryside more beautiful. Methodologically we rely on exploratory principal component analysis to study the structure of the preferences for individual freedoms and logistic regression analyses to test the hypotheses.

The paper is structured as follows. The first section discusses the theoretical debate on the association between the freedom and environment protection. (false) dilemma between economic growth and environmental protection and briefly summarizes the literature on environmental regulations, the role of the government and individual freedom. The next sections describe data and models. The following sections present and discuss the results. The last sections conclude.

2 Freedom and environment protection. The theoretical debate

Freedom and environmental sustainability are two concepts that are closely linked ( Hannis, 2015 ). Sustainable development is defined as “meeting the needs of the present without compromising the ability of future generations to meet their own needs” ( United Nations Brundtland Commission, 1987 ). To achieve this, it is essential that all members of society are able to make decisions freely and have access to resources so that they can make informed choices ( Boyle, 2007 ).

Environmental protection often conflicts with individual freedom ( Boyle, 2007 ; Shelton, 2012 ) though both are often seen as parts of human rights ( Osofsky, 2005 ). On one side of the debate, people argue that environmental protection must take precedence over individual freedom. Conversely, some argue that individual freedom should not be sacrificed in the name of environmental protection ( Boyle, 2007 ; Shelton, 2012 ). Those who argue in favor of prioritizing environmental protection over individual freedom say that the environment is a global resource that must be protected for future generations. They argue that individual freedom must be sacrificed to ensure that the environment is preserved and the global climate crisis is addressed. On the other hand, those who emphasize the importance of individual freedom argue that environmental protection should not come at the expense of individual rights and liberties. They argue that environmental regulations must be implemented in a way that does not overly restrict individual freedom ( Boyle, 2007 ; Shelton, 2012 ).

The debate over environmental protection and individual freedom is complex and difficult to resolve. It is important to recognize that both sides of the argument have valid points and that there is no easy answer. It is also important to recognize that the two sides of the debate are not mutually exclusive and that a compromise can be reached those respects both sides of the argument. For example, it is possible to implement environmental regulations in a way that does not overly restrict individual freedom while still achieving the goal of protecting the environment ( Klöpfer, 1996 ).

2.1 Economic growth or environmental protection? the (false) dilemma?

The role of environmental sustainability in limiting economic growth was first discussed in the aftermath of the Limits to Growth report ( Meadows, et al., 1972 ; Hannis, 2015 ). Leading economists widely recognized the depletion of non-renewable resources as a factor constraining long-term economic growth ( Solow, 1974 ; Stiglitz, 1974 ; Hartwick, 1978 ). The theories of sustainable development then emphasized limiting economic growth for the sake of environmental protection.

Environmental protection helps with many critical societal goals, such as long-term sustainability, a cleaner environment, reduction in climate change, and healthier food. However, it also requires additional resources and brings risks and limitations. It also creates new industries and promotes new technologies, which in the long run may increase economic performance ( Panayotou, 2016 ; Nikolaou, et al., 2021 ). On the firm level, better environmental performance can increase revenues via better access to particular markets, differentiating products, and selling pollution-control technology ( Ambec and Lanoie, 2008 ). Moreover, better environmental regulation increases resource use efficiency and, under some conditions, can increase economic performance (Porter hypothesis, Porter and Van der Linde, 1995 ; Brännlund and Lundgren, 2009 ). In addition, some factors, such as renewable energy, can positively impact both environmental protection and economic growth ( Hasanov et al., 2021 ); The total effect of environmental regulations on economic performance is unclear. The following Table 1 illustrates the two opposing views of literature on the topic.

TABLE 1 . The effect of environmental regulations on economic performance—two opposing views.

Ideally, environmental regulations should correspond to environmental quality. The relationships between economic growth and environmental quality may change the sign when the country reaches a certain level of economic performance as people can afford more efficient and environment-friendly production resulting in a cleaner environment as suggested by Environmental Kuznets Curve (EKC, Shahbaz et al., 2013 ; Stern, 2017 ; Anwar et al., 2022 ). Yet, environmental protection is a global issue, and especially the developed countries are introducing new measures to improve the environment.

3 The factors affecting the preferences for environmental protection. Literature review

The impetus for environmental protection was originally verbalized in the 1970s in the United States in Fisk’s Theory of Responsible Consumption ( Fisk,1974 ), Henion and Kinnear’s Ecological Marketing (1976), and Kardash’s Ecologically Concerned Consumer ( Kardash, 1974 ). Studies initially focused on energy use, pollution connected to the automobile, oil, and chemical industries, as well as consumer reactions to advertising and labeling ( Henion and Kinnear, 1976 ; Kilbourne and Beckmann, 1998 ; Peattie, 2010 ). Subsequently, they turned to examine green purchases of food products and environmentally friendly items.

Research into the preferences for environmental protection has focused on identifying impacting factors to promote environmental protection. These factors have largely reflected the prevailing social and economic paradigms of the time. Early literature concentrated on economic incentives and financial capabilities of households, socio-demographic characteristics ( Laroche, et al., 2001 ; Robinson and Smith, 2002 ; Jenkins, et al., 2003 ), and environmental knowledge ( Peattie, 2010 ) and advised that government policy should primarily provide economic incentives to support pro-environmental behavior ( Bartelings and Sterner, 1999 ; Eriksson, 2004 ; Jackson, 2005 ; Wang et al., 2021 ; Shen and Wang, 2022 ). This approach is still in use today in waste management, where households are incentivized to sort communal waste by making the disposal of sorted waste free of charge. The socio-demographic factors as potential predictors of preferences for environmental protection are often used as control variables in more recent studies ( Walia et al., 2020 ). The studies based on economic data suggested that more affluent households have a greater environmental footprint yet can afford to buy “greener” products ( Cymru, 2002 ; Lenzen and Murray, 2003 ; Huang et al., 2022 ). Therefore, a rise in income may lead to an increase in pro-environmental consumption.

After focusing on economic, demographic, or knowledge factors, the research has shifted its focus to attitudes and values, which were recognized to be often more important in predicting environmental protection than economic or socio-demographic. For example, Schwartz’s value model and altruistic values have been reported to be linked to pro-environmental behavior ( Han et al., 2007 ; Carrus et al., 2008 ; Peattie, 2010 ; Wang L. et al., 2019 ; Wang Y. et al., 2019 ). Surprisingly, not all pro-environmental values lead to greater environmental protection. For example, pro-environmental values may not always lead to an increase in such activities as recycling ( Barr, 2007 ), buying organic food, or avoiding leaving appliances on standby ( Lyndhurst, 2004 ). Research has also indicated that environmental attitudes, environmental knowledge, subjective norms, perceived behavioral control, conditional value, and emotional value all positively affect pro-environmental intentions and behaviors ( Nekmahmud et al., 2022 ).

3.1 Government regulations, freedom, and environmental protection

Governmental regulations are frequently called upon to ensure environmental protection ( Sarkodie and Strezov, 2019 ; Güngör et al., 2021 ; Al-Mulali et al., 2022 ). However, restrictive governmental regulations “circumscribes the autonomy (freedom) of the members of society” ( Porket, 2003 , p. 50). The post-soviet countries present a wide variety of attitudes to personal freedoms ranging from more Westernized democratic Baltic countries admitted to European Union to a collection of autocracies without any extensive, market-based liberalization in Central Asia ( Hartwell, 2022 ).

Economic and political freedoms have been shown to affect the environment significantly regarding the preferences for and costs of environmental protection ( Zhang et al., 2019 ; Bruun, 2020 ; Halvorson, 2021 ; Anwar et al., 2022 ). Yet, the preferences for political and economic freedoms are rarely considered for predicting the environmental preferences of the population ( Joshi and Beck, 2018 ). In this paper, we hypothesize that the preferences for individual freedoms are significant predictors of the preferences for environmental protection (H1).

Environment protection requires regulation of personal behavior, which can be monitored via all kinds of surveillance means, including street cameras, monitoring of emails, and collecting and storing personal information. These means can increase the efficiency of environmental regulations but decrease individual freedoms. In this paper, we hypothesize that the preferences for government-managed video surveillance, monitoring of emails, and collecting information about everyone predict preferences for environmental protection (H2).

Personal freedoms are often exchanged for (the illusion of) protection from the government ( Hofstede, et al., 2005 ). We test whether the preference on the amount of government responsibility (government taxing the rich and subsidizing the poor, making the incomes equal, government owning the businesses, government paying unemployment benefits, people obeying their rulers) predicts preferences for environmental protection.

Personal freedom is also reflected in the procedure of election. We hypothesize that the preferred role of the government and the way it is elected are significant predictors of the preferences for environmental protection (H2). We employ the following indicators to account for the election procedure: people choose their leaders in free elections, the importance of democracy, personal freedoms as a sign of democracy, women have the same rights as men, and the army takes over when the government is incompetent (disagreement with).

3.2 The role of religion

The post-soviet region is largely diversified in religious confessions and the role assigned to God. The scale ranges from relatively secular Baltic countries (Estonia, Latvia, Lithuania), through multi-religious Russia, to essentially 90% religious Islamic (mostly) Central Asia ( Simons and Westerlund, 2016 ). After the fall of the Soviet Union, religious confessions gained more power in defining, interfering and affecting the ideas of personal freedom and the environment ( Froese, 2004 ).

Religion has a strong influence on people’s preferences to protect the environment. Many religious teachings incorporate conservation and stewardship of the environment, providing an ethical and moral incentive to protect the environment ( Djupe and Hunt, 2009 ; Jenkins and Chapple, 2011 ). Religious beliefs can also shape people’s attitudes toward the environment in terms of the value they place on nature, the importance of maintaining a balance between humanity and nature, and the need to be good stewards of the Earth ( Jenkins and Chapple, 2011 ; Hope, and Jones, 2014 ; Bergmann, 2017 ). This can lead to an increased commitment to environmental protection and conservation, as well as greater environmental concern and activism ( Sherkat and Ellison, 2007 ). Thus, in the line of Eom, et al. (2021b) , we suggest that religiosity is a significant predictor for the preferences for environmental protection in post-soviet countries (H3). We employ two indicators for religious beliefs: the subjective importance of God in life and the level of agreement with the religious authorities interpreting the laws.

4 Materials and methods

4.1 the study.

This paper aims to study the impact of preferences for economic (and other) freedoms and the expected role of the government on preferences for environmental protection in the eleven Post Soviet Union countries (Azerbaijan, Armenia, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Lithuania, Russia, Tajikistan, Ukraine). Religiosity is suggested to be the next factor to consider. The following hypotheses are tested:

H1. Preferences for individual freedoms predict preference for environmental protection.

The indicators of the preferences for individual freedoms include the preferred right of the government to:

• Keep people under video surveillance in public areas.

• Monitor all emails and any other information exchanged on the internet.

• Collect information about anyone living in the country without their knowledge.

H2. The preferred role of the government predicts preferences for environmental protection. The Indicators for the role of the government include:

• Governments tax the rich and subsidize the poor.

• Religious authorities interpret the laws.

• People choose their leaders in free elections.

• People receive state aid for unemployment.

• The army takes over when the government is incompetent.

• Civil rights protect people’s liberty against oppression.

• Women have the same rights as men.

• The state makes people’s incomes equal

• People obey their rulers

H3. Religiosity affects the preference for environmental protection.

The indicators for religiosity include.

• Importance of God in life

• Religious authorities should interpret the laws

As economic performance is of immense importance in Post-Soviet countries, we also test similar hypotheses to predict the preferences for economic growth as one of the country’s priorities. This will enable us to contrast the importance and effects of environmental protection to the other social goals.

4.2 The data

We employ a representative dataset collected in the World Value Study and European Value Study in 11 post-Soviet Union countries in 2017–2020 (Joint dataset, EVS/WVS, 2021 ; see also EVS, 2020a ; EVS, 2021 ; Haerpfer et al., 2021 ). The choice of countries was based on data availability. All the Post-Soviet Union countries present in the EVS/WVS dataset were incorporated into the analysis. The target population was defined as persons aged 18 and older who had been residing in the country within private households for the past 6 months before the fieldwork ( EVS, 2020b ; WVS, 2020 ). The sampling relied on a representative single-stage or multi-stage probability sampling of the country’s adult population, 18 years old and older. The sample size was set as an effective sample size: with N minimum of 1,500 for countries over 100 million, 1,200 for countries with a population over 2 million, and 1,000 for countries below 2 million. A resulting total sample embraced 20006 respondents aged 18+ (mean age ± SD: 46,04 ± 17,07, 58% women, 46,8% upper education (Upper level: ISCED 2011 levels 5–8—short cycle tertiary and higher). Most surveys were conducted using face-to-face interviews ( WVS, 2020 ; EVS, 2020b ) The data are available for non-commercial purposes at the web pages of European and World Value Studies ( https://europeanvaluesstudy.eu/methodology-data-documentation/survey-2017/joint-evs-wvs-2017-2021-dataset/ , accessed 11.11.21).

4.3 Indicators

The following section provides the exact wording of the questions employed in the further analysis and the distribution of the respondents.

4.3.1 Preference for environmental protection at the expense of economic growth

4.3.1.1 protecting environment vs. economic growth.

“Here are two statements people sometimes make when discussing the environment and economic growth. Which of them comes closer to your own point of view?

• Protecting the environment should be given priority, even if it causes slower economic growth and some loss of jobs.” (53,70% of the respondents),”

• Economic growth and creating jobs should be the top priority, even if the environment suffers to some extent. (46,30% of the respondents)" ( EVS, 2020a ; 2021 ; Haerpfer et al., 2021 ).

The distributions of the respondents in studied countries are presented in Figure 1 below (end of the paper) and Supplementary Table SA3 .

FIGURE 1 . Protecting the environment vs. economic growth. The distribution of the respondents. Source: own computations based on the data EVS/WVS ( EVS, 2020a ; EVS, 2021 ; Haerpfer et al., 2021 ).

4.3.1.2 Economic growth as one of the country’s priorities

• “A high level of economic growth” (57,20% of the respondents)

• “Making sure this country has strong defense forces” (21,40% of the respondents)

• “Seeing that people have more say about how things are done at their jobs and in their communities (14,90 of the respondents)”

• “Trying to make our cities and countryside more beautiful (6,50% of the respondents)", ( EVS, 2020a ; EVS, 2021 ; Haerpfer et al., 2021 )

“People sometimes talk about what the aims of this country should be for the next ten years. On this card are listed some of the goals that different people would give top priority. Would you please say which one of these you, consider the most important?”

Figure 2 below and Supplementary Table SA4 present the distributions of the respondents in countries.

FIGURE 2 . Aims of the country, first choice. The distribution of the respondents. Source: own computations.

4.3.1.3 Personal freedom versus the role of the government

This study considers the capability of the government to control individual lives via video surveillance, monitoring the information exchanged on the internet, and collecting information about individuals without their knowledge. The corresponding question in the questionnaire was formulated as follows:

• Keep people under video surveillance in public areas

• Monitor all emails and any other information exchanged on the internet

• Collect information about anyone living in [COUNTRY] without their knowledge

1 - Definitely should have the right; 2—Probably should have the right; 3—Probably should not have the right; 4—Definitely should not have the right” ( EVS, 2020a ; EVS, 2021 ; Haerpfer et al., 2021 )

“Do you think that the [COUNTRY] government should or should not have the right to do the following :

We suggest that all three questions are related to preferences for environmental protection. For example, monitoring people in public areas might be used as a tool to localize and personalize the origins of garbage lest on unauthorized places. The monitoring of the emails and collecting information may provide information on intentions to comply with government regulations to protect the environment.

Personal freedom goes hand in hand with personal responsibility. The corresponding questions in the questionnaire were formulated as follows:

• People should take more responsibility; 10- The government should take more responsibility

• Private ownership of business should be increased; 10- Government ownership of business should be increased” ( EVS, 2020a ; 2021 ; Haerpfer et al., 2021 )

“On this card you see a number of opposite views on various issues. How would you place your views on this scale?

The distribution of the respondents is presented in Supplementary Table SA5 .

4.3.1.4 Personal freedoms and rights as essential signs of democracy

• People obey their rulers” ( EVS, 2020a ; EVS, 2021 ; Haerpfer et al., 2021 )

“Many things are desirable, but not all of them are essential characteristics of democracy. Please tell me for each of the following things how essential you think it is as a characteristic of democracy. Use this scale where 1 means “not at all an essential characteristic of democracy” and 10 means it definitely is “an essential characteristic of democracy.”

The distributions of the respondents are presented in Supplementary Table SA6 .

4.3.1.5 The level and importance of democracy

“How important is it for you to live in a country that is governed democratically? On this scale where 1 means it is “not at all important” and 10 means “absolutely important,” what position would you choose?” ( EVS, 2020a ; EVS, 2021 ; Haerpfer et al., 2021 )

“And how democratically is this country being governed today? Again using a scale from 1 to 10, where 1 means that it is “not at all democratic” and 10 means that it is “completely democratic,” what position would you choose?” ( EVS, 2020a ; EVS, 2021 ; Haerpfer et al., 2021 )

The distributions of respondents are presented in Supplementary Table SA7 .

4.3.1.6 The attitude to competition and work

Environmental restrictions highly affect the competitiveness of the firms and the availability of jobs ( Iraldo, et al., 2011 ; Dechezleprêtre and Sato, 2017 ; Borsatto and Amui, 2019 ). We control for the attitude to competition (good-harmful) and the importance of work and equal pay. The answers to the following questions are used as indicators.

• Competition is good, 10—competition is harmful

• Incomes should be made more equal, 10—We need larger income differences as incentives” ( EVS, 2020a ; 2021 ; Haerpfer et al., 2021 )

• Work. 1—Very important; 2—Rather important; 3—Not very important; 4—Not at all important.” ( EVS, 2020a ; EVS, 2021 ; Haerpfer et al., 2021 )

“On this card, you see a number of opposite views on various issues. How would you place your views on this scale?

“Please say, for each of the following, how important it is in your life.

The distribution of the respondents is presented in Supplementary Table SA8 .

4.3.1.7 Importance of God and socio-demographic characteristics

Following Eom, et al. (2021b) , we study the effect of religiosity on preference for environmental protection at the expense of economic growth. The question was formulated as follows:

• Please use this card to indicate—10 means very important and 1 means not at all important.” ( EVS, 2020a ; EVS, 2021 ; Haerpfer et al., 2021 )

“And how important is God in your life?

The resulting variable presented mean of 7,57 and Std. Deviation of 3,175. A total sample embraced 20006 respondents aged 18+ (mean age ± SD: 46,04 ± 17,07, 58% women, 46,8% upper education, the distribution of the respondents split by countries see Supplementary Table SA2 ).

4.4 The method

First, we conducted an exploratory Principal Component Analysis to study the perceptions of individual freedoms as signs of democracy. Then we rely on ordinal regression analysis to test the hypotheses ( Formula 1 , the numbers like a 1-13 denote thirteen coefficients corresponding to thirteen indicators of preferences for freedom versus government, see the description of the variables beneath the equation)

Environment vs. Growth i

• two indicators of preferences for environment vs. economic growth and economic growth as a country priority subsequently.

Freedom versus government

• Government should have the right to monitor people via internet, in public areas and collect information without their knowledge.

• Government should tax the rich and subsidize the poor.

• People have the freedom of election.

• People have the right to state aid for unemployment.

• In case of an incompetent government, the army takes over

• Civil rights protect people’s liberty

• Gender equality of rights and freedoms

• More income equality

• People need to obey their rulers

• People should take more responsibility, not the government

• Private or government business ownership is preferable

• Democracy is important

• The country is democratic

Competition and work

• Competition is good/harmful

• Incomes should be more/less equal

• Importance of work

Religiosity

• The importance of God in life

• country dummies for Azerbaijan, Armenia, Belarus, Estonia, Georgia, Kazakhstan, Kyrgyzstan, Lithuania, Russia, Tajikistan, Ukraine

Socio-demographic characteristics

• Age

• Gender

• Education

The two models corresponding to two dependent variables were estimated via ordinal logit regression ( Formula 1 ). The Pearson correlations of independent variables are presented in Supplementary Table SA1 . None of the correlations exceeded 0,5; thus, multicollinearity is unlikely.

5.1 Individual freedoms as characteristics of democracy. The results of the Principal Component Analysis

Before discussing the results of the Principal Component Analysis, we present the setting of the analysis and the indicators measuring the suitability of the data for this type of the analysis. The Principal Component Analysis was set as follows: rotation Method - Varimax with Kaiser normalization; the number of components - according to Eigenvalue (>1). Rotation converged in 3 iterations. The Bartlett test of sphericity with a Chi-Square value 106609,60 ( p < 0,001) and Kaiser-Meyer-Olkin Measure of sampling adequacy with a value equal to 0,790 (>0,6) suggests that the data are suitable to identify factor dimensions. The indicators of applicability of the Principal component analysis, as presented above, suggest that the method is suitable for the data.

The results of the Principal Component Analysis are presented in Tables 2 , 3 . Four extracted components altogether were able to explain 51,29% of the variance.

TABLE 2 . Principal component analysis for individual freedoms as signs of democracy. Total variance explained.

TABLE 3 . Principal component analysis for individual freedoms as signs of democracy. Rotated Component Matrix.

As the results suggest, the indicators for freedom (as a sign of democracy) divided themselves into two categories described by two latent variables ( Table 3 ). The first views democracy as a system representing civil rights and freedoms, implying free elections, gender equality, liberty, and the right to receive state aid if unemployed. The other group of variables describes democracy in terms of the increased role of the state, army, and religion, implying the state provides more income equality. If the state is incompetent, the army takes over, religious authorities interpret the laws, and the population is obedient to their rulers. The first latent variable constitutes the freedom apex, while the second constitutes the opposite.

Personal freedoms as predictors of preference for environmental protection and economic growth. The results of logistic regression analyses.

The results of logistic regression analyses ( Formula 1 ) are presented in Table 4 .

TABLE 4 . Predicting environmental protection vs. economic growth, economic growth vs. other goals. The results of ordinal regressions.

The summary of the statistically significant results from Table 4 is presented in Table 5 . The positive associations are denoted by "+", the negative ones, by “-”.

TABLE 5 . Summary of results of ordinal regressions ( Formula 1 ; Table 5). Statistically significant associations.

6 Discussion

6.1 hypotheses 1 and 2: preferences for individual freedoms and the role of the government predict preferences for environmental protection..

This paper studied the association between the preferences for individual freedom, the role of the government, and preferences for environmental protection. The results of the analysis above indicate that associations between environmental protection, economic growth, and individual freedoms are far from uniform. On one side, personal freedoms (civil rights, the importance of democracy, gender equality, pay inequality if it occurs, no role of the army in politics) predicted higher preferences for environment protection at the expense of economic growth and higher growth itself as opposed to other societal goals. This indicates personal freedoms are positively related to environmental protection. On the other side, governmental video surveillance in public areas showed to be positively related to both environmental protection and economic growth. However, the right of the government to internet monitoring decreased preferences for economic growth but not for environmental protection.

The ambivalence above poses questions about the right type of freedom and control affecting environmental and economic outcomes. As individual freedoms start and end with the freedoms of others, we can hypothesize that the preference for video surveillance in public places corresponds to the need to monitor the activities of fellow citizens, traffic, and other features of the outer environment. In the case of environmental protection, it is understandable as it allows more efficient environment monitoring and enforcement of environmental regulations. As concerned with economic growth (the country’s priority), video surveillance ensures more safety ( Sharma et al., 2022 ), more efficient crime abatement ( Garibotto, 2010 ), and rule enforcement ( Yesil, 2006 ).

Though video surveillance violates some human rights for privacy ( Granholm, 1986 ), it is considered one of the most effective means for an emergency response to traffic or the environment ( Noguera et al., 2011 ; Chung, 2012 ; Chen, et al., 2014 ). Video surveillance is also one of the most effective ways for real-time environment control ( Stipanicev, et al., 2007 ) and an essential feature of smart cities ( Korchani and Sethom, K. 2021 ).

Environmental regulations substantially disturb competition ( Iraldo et al., 2011 ; Dechezleprêtre and Sato, 2017 ; Borsatto and Amui, 2019 ), though there are considerable efforts to integrate green policy into competition legislation ( Kingston, 2010 ). However, our results report that the importance of competition significantly predicted a preference for environmental protection at the expense of economic growth and the preference for economic growth as a priority over other goals. This ambivalent result is still to be explained. Besides competition, the importance of work in life predicted a preference for economic growth.

6.2 Hypothesis 3: Religiosity predicts the preference for environmental protection.

The importance of God showed to positively predict environmental protection and negatively predict economic growth. The matter of environment is of immense importance in religious beliefs. In Islam, the environment bears much importance, and the rights and responsibilities of a man with respect to the environment are clearly stated ( Omer, 2012 ). In Christianity, the belief in a controlling god is significantly associated with environmental guilt ( Eom, et al., 2021a ) and environmental justice forms one of the principles of eco-theology ( Hrynkow, 2017 ). Surrendering Environmental Identities is viewed as one of the ways of becoming one with God ( Roshani and Rathnasiri, 2018 ). The importance of God appears to be one of the significant predictors of environmental preferences, which should not be forgotten. On the other hand, the intrusion of religious authorities into secular processes in interpreting the laws showed to predict lower preferences for economic growth.

6.2.1 The country differences

Azerbaijan, Belarus, Georgia, Lithuania, and Ukraine report higher importance of economic growth as the most important aim of the country, while Estonia presented lower. Oppositely, Azerbaijan, Estonia, Georgia, Kyrgyzstan, and Ukraine showed more preference for environmental protection at the expense of economic growth compared to Russia, while Armenia, Belarus, Lithuania, and Tajikistan reported more preference for economic growth at the expense of environmental protection compared to Russia (controlling for all the variables presented in table Results).

6.2.2 Age, gender, education

Women prefer more environmental protection at the expense of economic growth compared to men. People with lower education place less importance on economic growth than higher-educated people.

7 Conclusion

The association between economic development and environmental degradation generally follows the U shape titled Environmental Kuznets Curve (EKC, Shahbaz et al., 2013 ; Stern, 2017 ; Anwar et al., 2022 ). Lower-income countries generally reside on the beginning part of the curve, meaning that economic development damages the environment, while more well-to-do countries present a more favorable increasing relationship between economic development and the state of the environment. The Post Soviet countries generally belong to the first part of the curve, meaning that economic development, if not corrected by environmental regulations, increases environmental pollution levels and generally damages the environment ( Yang et al., 2017 ; Hasanov et al., 2019 ; Hasanov et al., 2023 ). Especially in these countries, the environment protection measures go against economic performance, and the tradeoff between more economic growth and environmental protection is more pronounced.

In this paper, we run Principal Component Analysis to study the structure of preferences for personal freedom and conducted logistic regression analyses to study the effects of preferences for individual freedoms on preferences for environmental protection at the expense of economic growth and economic growth as one of the country’s priorities. We employed a representative sample from eleven Post Soviet Union countries (N = 20006, age 18+, M ± SD: 46,04 ± 17,07; 58% women, 46,8% upper education).The results suggest that personal freedoms (civil rights, importance of democracy, gender equality, income inequality, no role of army in politics) predicted preferences for environmental protection at the expense of economic growth and growth as opposed to other societal goals. However, the right of the government to surveillance in public areas, though diminishing personal freedoms in terms of anonymity, proved to be positively related to both environmental protection and economic growth as one of the country’s priorities. Though environmental regulations generally decrease the firm competitiveness, the preferences for competition proved to predict higher preferences for environmental regulations.

Last but not least, religious beliefs proved to predict higher preferences for environmental protection but lower preferences for economic growth. In fact, in many religions, God is considered a part of the environment, and the rights and responsibilities of man to the environment are the central part of religious beliefs ( Omer, 2012 ; Hrynkow, 2017 ; Eom, et al., 2021a ). The role of religion in shaping individual preferences needs more research.

Overall, the results supported the view that even though environmental regulations generally reduce individual freedoms and obstruct economic performance in many cases, they are in line with the preferences for individual freedoms in many aspects. This may indicate the increasing understanding of a cleaner environment as an individual right that widens the spectrum of preferred individual freedoms. This result is rather optimistic, especially in the set of the Post Soviet Union countries, many of which are still struggling economically and yet consider the environment as a part of (or at least in line with) their individual freedoms.

These results suggest several implications. First, though environmental regulations may harm particular firms, society views the benefits it provides as a part of their freedoms. If communicated correctly, the measures are likely to gain social support. Second, the support for environmental protection measures should be studied jointly with other preferences for individual freedoms as they seem to form a specific system. Third, the broad society seems to be aware of environmental impacts and, to at larger extent, recognizes the role of the environment even at the expense of economic growth. Thus the government may communicate the need for environmental protection as a part of individual freedoms for a clean environment.

Data availability statement

Publicly available datasets were analyzed in this study. This data can be found here: https://www.worldvaluessurvey.org/wvs.jsp .

Ethics statement

The studies involving human participants were reviewed and approved by Ethics committee Czech University of Life Sciences, Prague. The patients/participants provided their written informed consent to participate in this study.

Author contributions

Conceptualization, IC and LS; methodology, IC; data curation, AO; writing—original draft preparation, AO and DM; writing—review and editing, AO, LS, IC, DM, and SK; supervision, LS; project administration, LS; funding acquisition, LS. All authors have read and agreed to the published version of the manuscript. All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

The paper supported by the internal research Project No. 2021B0002: The post-Soviet Region in the Context of International Trade Activities: Opportunities and Threats Arising from Mutual Cooperation, solved at the Department of Economics, Faculty of Economics and Management, Czech University of Life Sciences in Prague.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fenvs.2023.1129236/full#supplementary-material

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Keywords: environment protection, economic growth, preferences, survey, environmental kuznets curve, post-soviet countries

Citation: Čábelková I, Smutka L, Mareš D, Ortikov A and Kontsevaya S (2023) Environmental protection or economic growth? The effects of preferences for individual freedoms. Front. Environ. Sci. 11:1129236. doi: 10.3389/fenvs.2023.1129236

Received: 21 December 2022; Accepted: 09 May 2023; Published: 22 May 2023.

Reviewed by:

Copyright © 2023 Čábelková, Smutka, Mareš, Ortikov and Kontsevaya. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Inna Čábelková, [email protected]

Essay on Environment and Economic Growth

Essay on Environment and Economic Growth!

As E.O Wilson has put it:

Environmentalism sees humanity as a biological species tightly dependent on the natural world. Many of earth s vital resources are about to be exhausted, if atmospheric chemistry is deteriorating and human populations have already grown dangerously large. Natural ecosystems the well-springs of a healthful environment are being irreversibly degraded.

Believers in this dismal picture argue that humans must practice “sustainable” economic growth and learn to live within the limitations of our scarce natural resources-or we will suffer irreparable consequences. Humans have been encroaching the physical environment for ages, over the years.

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The major interventions occurred, when humans moved into settlements and convert forests into farmland and started to domesticate animals and plant trees. But this qualitative transformatic pales beside today’s massive bioengineering, deforestation and extracts of mineral and plant resources from the earth (in its Limits to Growth).

In this context the Club of Rome made the following predictions:

If present growth trends in world population, industrialisation, pollution, food problems and resource depletion continue unchanged, the limits to growth on this planet will be reached within the next one hundred years. The most probable results will be a rather sudden and uncontrollable decline in both population and industrial capacity.

As humans spread around the globe, they tend to displace trees, wolves and marsh weeds to make way for farms, cities and human settlements. Many of the Earth’s vital resources are about to be exhausted, its atmospheric chemistry is deteriorating and human population have already grown dangerously large [6.53 billion (= 653 crores) in 2006], Natural ecosystems, the wellsprings of a healthy environment— are being irreversibly degraded. Economic growth and industrialisation are roads to environmental ruin.

Growing degradation in natural resources is now a serious problem of LDCs. This is the result of the interactions between the traditional sector (due to strong population pressure on limited land resources) and modern sector (with increased capital intensity through technology borrowing). This has resulted in growing poverty and inequality in the early stages of develop­ment of today’s LDCs. The environmental problem may be defined as the problem of natural resources exhaustion resulting from exploitation at speeds beyond their natural recovery rates, which endangers sustenance of life.

Poverty and Environmental Degradation :

The root cause of environmental degradation in LDCs is the growing incidence of poverty. Most people in LDCs, particularly in rural areas, do not have any private property. So they have to depend on certain common property resources.

There is no clearly defined legal right on such resources. As-a result, someone’s tree-cutting significantly reduces other’s opportunities of forest use, more so in view of the fact that forests are becoming scarce owing to population growth and economic activities (industrialisation and urbanisation).

Environmental problems are really serious in LDCs because changes in technology and institutions lag behind changes in resource endowments. With rapid growth of population, resources become more and more scarce.

At the same time, institutions for conserving scarce natural resources have been slow to develop. These two developments conjointly led to the serious depletion of common-property resources. This lag in institutional adjustment tends to become larger in LDCs due to poverty. In other words, poverty is the main cause of environmental destruction.

Rural Poverty and Environmental Destruction :

The main force behind environmental degradation in LDCs is pauperization of the rural popu­lation due to population pressure. As the supply of fertile land becomes scarce relative to in­creased population in traditional agriculture, poor people are forced to cultivate fragile land for subsistence in hills and mountains. This results in a high incidence of soil erosion.

In addition, they are forced to cut forests for timber and fuel as well as graze animals on pasture lands, exceeding the reproductive capacity of these natural resources. It is quite obvious that, in such a situation, dire poverty or destitution typically becomes a vicious circle.

Poverty results in malnutrition and reduces the poor people’s capacity for work, precluding them from gainful employment opportunities. They are thereby forced to rely more heavily on the exploitation of fragile natural resources in marginal areas, to which property rights are not assigned.

In order to prevent such environmental destruction due to rural poverty it is necessary for the government to regulate the use of environmentally fragile areas. However, if regulations are effectively enforced, a means of subsistence for the poor would disappear altogether.

How­ever, the real solution to the problem lies in increasing employment and income by improving the productivity of the limited land already in use. This solution implies shifting from tradi­tional resource-based to modern science-based agriculture, as symbolized by the Green Revo­lution.

The Green Revolution, however, has been criticised for environmental reasons, e.g., di­rected against fertilizers and chemicals — that poison the soil and water causing ecological and human health damage. Furthermore, irrigation without adequate drainage facilities tends to result in soil degradation through salinity and water logging.

However, if for all these reasons the efforts to develop modern technology were abandoned then employment and income-generating opportunities for marginal farmers and landless agri­cultural labourers would gradually disappear in the face of growing pressure of population on land. As a result, many would be forced to push cultivation frontiers into ecologically fragile land, resulting in increased incidence of flood and soil erosion.

Therefore, it is necessary to overcome the defects of modern agricultural technology by strengthening scientific research. In addition, it is not in the Tightness of things to restrict the distribution of agricultural technology to favourable production environments with good irri­gation conditions. Instead, it has to be extended to both productivity increases and environ­mental conservation in fragile areas through such means as agro-forestry and complementary use of arable lands and grasslands.

No doubt rapid population growth in the face of low total factor productivity is the root cause of poverty in most LDCs. And growing incidence of poverty is the root cause of environ­mental degradation. In this context, W. Beckerman has made the following comment on the relationship among population, economic development and pollution:

“The important environmental problems for the 75% of the world’s population that live in developing countries are local problems of access to safe drinking water or decent sanitation, and urban degradation. Furthermore in the end the best and probably the only way to attain a decent environment in most countries is to become rich.

The economy and the environment are complex interdependent systems. Continued eco­nomic growth and even human survival depend on natural resources used in production and on the life-supporting services of natural ecosystems. But overuse of natural resources and discharging polluted waters into the environment may threaten those ecosystems.

Thus soci­eties require feedback mechanisms to signal the health of their combined economic and en­vironmental systems and to take timely corrective actions; otherwise economic growth will not be sustainable and the growth and life supporting services of the environment will not continue as economic activity expands.

Urban Poverty and Environment :

For both rural and urban areas the poor are the first to be endangered by environmental degradation. If this damage to poor people coincides with unequal income distribution, social and political stability—the basis of economic growth—will be severely undermined.

If left unchecked, environmental degradation due to pollution tends to progress cumulatively and will have devastating consequences in the long run. It is, therefore, of strategic significance for developing countries to lower the peaks of the environmental Kuznets curve in order to sustain their economic growth.

Pollution arising from industrialisation and urbanisation can be suppressed in developing countries to a much lower level than experienced by advanced economies in the past if technologies and know-how accumulated in the latter are effectively applied to the former.

It is not much difficult to counteract environmental degradation by designing the institutions and policies to promote adoption of anti-pollution technologies. The core of the environmental problem is the divergence between private and social costs in the use of the environment, which induces overuse of environmental resources or exploitation of such resources above socially optimal levels. Therefore, the environmental problem can be solved by raising the private cost of utilizing the environment (such as discharging noxious gas into the air) relative to social cost.

The Sustainability Issue :

The interactions between the economy and the environment prompt the question of whether over time continued expansion of economic activity is consistent with ecological stability — with continued functioning of the ecosystem on which all human activities and life system ultimately depend.

A growing economy will use natural resource inputs and discharge wastes, progressively changing the environment on which it depends. The resulting reduction in the quality and quantity of natural inputs, waste sinks, amenities and life support services will endanger continued growth and gains in human welfare, perhaps even human survival, unless timely corrective actions are taken.

How do we achieve continued compatibility between economic decisions and environ­mental service flows as economic activity expands? This is the genesis of the modern concept of sustainability.

For most economists, sustainability is:

1. Seeking to ensure that current economic decisions take full account of economy environment interactions, now and in the future;

2. Concern about the well-being of people in both present and the future, involving both meeting the needs of the present and preserving the capacity of the future generations to be no less well off than the present generation.

Two Types of Sustainability :

In the opinion of R. M., Solow, sustainability is achieved not by preserving specific natural resources, but by maintaining a broad aggregate of natural and created capital. This is the concept of weak sustainability.

Some environmental economists take the view that the ability of created capital to substi­tute for natural resources is limited, in particular, in the case of ecological life support services on which all planetary life ultimately depends. This leads to the concept of strong sustainability.

Strong sustainability requires the maintenance of an aggregate of natural capital or the protec­tion of special natural capital believed essential to the well-being of people in the future. Effective implementation of both strong and weak sustainability imposes additional infor­mation demand on planners; the need to value different items of natural and created capital and possibly in the case of strong sustainability, the ability to identify the specific natural capital essential to future well-being.

Other Views of Sustainability :

Ecologists identify sustainability with ecological resilience—the ability of ecosystems to main­tain their physical and biological functioning after disturbance. An ecosystem is resilient and, therefore, sustainable, if it can reestablish it, with its biological functioning, if not all of its constituent species—unchanged after a cyclone or a volcanic eruption or an oil spill.

Ecosystem resilience does not require stability or even survival of all the ecosystem’s con­stituent species, including humans. Humanity is just one species living in and deriving life support from ecosystems. The dissonance between economist’s and ecologist’s conceptions of sustainability brings into focus the important point that for most people, sustainability is a human-centred, rather than a nature-centred concept. The environment may change, but it should not change so much as to endanger human lives or living standards.

According to most ecologists, this type of stability is not a natural property of environmental systems; rather these are dynamic and evolve over long periods of time. Humans may be more comfortable with the notion of a stable envi­ronment, but, in reality, the processes of environmental change are chance driven, with no inher­ent stability. And, since we live in a world governed by chance, we cannot calculate what nature will throw up next; sustainability policies that aim at desired future states of the world are not necessarily in harmony with nature.

Empirical studies show that pollution trends tend to follow an inverse U-shaped curve across different stages of economic development. See Fig. 1. At low levels of income at E, subsistence agriculture generates hardly any pollution. Then, with initial stages of development, the growth of heavy industries increases pollution control, leading to higher per capita pollution at F.

The rising part of the curve occurs because urbanisation, accompanied by the growth of highly polluting industries, often replaces agriculture in the early stages of development. As steel plants replace subsistence farming, it is nearly inevitable that air pollution will become worse, particularly in low income countries which cannot afford much pollution abatement.

Finally, with pollution abatement and the trend away from industry and towards services in advanced countries, pollution decreases at G. As income rises, countries tend to invest in pollution abatement and their economic structures evolve towards services and away from heavy industries, reducing pollution. This can explain the inverted U-shaped pollution curve, also called Environmental Kuznets Curve.

The environment is vital for all of us because it provides a life support system. It provides inputs for production of economic goods and services. It also acts as a waste sink. However, in the last five decades there has been growing concern about the effect of economic activity on the physical environment.

It has been argued that economic growth has caused serious environmental damage and the current state of the environment will hamper future economic development. The poor in developing countries are often depending on the natural environment for their livelihood and even their continued existence. The damage to the environment and the relationship between the environment and the economy are often thought to be of more importance to developing countries.

Economic Growth and the Environment :

Environmentalists have argued that unconstrained economic growth will lead to the exhaustion of non-renewable resources and to levels of environment degradation that will seriously affect production of desirable goods and services and the quality and existence of life.

It has been suggested that in the early stages of economic development the level of environmental degradation increases, but after this phase the environment improves with economic development. This behavioural pattern is captured by the U-shaped environ­mental Kuznets’ curve, as shown in Figure 1.

Sustainable Development :

It has been widely held that present patterns of economic growth may seriously degrade the environment and may be unsustainable, as the environment cannot support economic growth forever. It is alleged that past and present economic policies have usually been concerned with providing the conditions for economic growth, as measured by standard national accounting methods.

Many environmentalists are concerned that these policies have not attempted to ensure the existence of ecological conditions necessary to support human life at a specified level of well-being through future generations.

This concern is of major importance in the concept of sustainable development. SD has become perhaps the most important approach as the relation between the environments on development is concerned. According to the Brundland Report (1987), “SD seeks to meet the needs are aspirates of the present without compromising the ability of future generations to meet their own needs.”

The concept of SD has become a standard model for thinking about the environment development and the economy. The concern for equity between and within generations is central to most interpretations of the concept.

Natural Capital, Equity and Environment :

For ensuring substantially, the stock of capital has to be preserved (i.e., it should be allowed to decline over time). A constant (increasing) stock of capital will permit consumption levels to be maintained (increased). In this context we may refer to two views on sustainability. The weak sustainability view treats all the different forms of capital (e.g., man-made, human, natural and social) as substitutes.

So they can be aggregated to form total capital. Thus, for example, degrading the natural fertility of the soil can be compensated for by using chemical fertilizers and modern scientific methods to maintain (or even raise) yield per hectare. This means that human and man-made capital are used as substitutes for natural capital.

The strong sustainability view takes the position that it is only natural capital that needs to be held constant or increased. The focus, according to this view, is often on critical natural capital which is either required for human survival or cannot be substituted for with other forms of capital. Thus one might take atmospheric carbon dioxide levels as critical natural capital as higher levels cannot be offset by other types of capital.

Preserving of increasing the stock of natural capital has important effects on inter-generational equity. If it is believed that present level of environmental degradation and resource use will substantially alter future human economic welfare, then just by preserving natural capital intergenerational equity may be improved. This is the strong sustainability view.

However, the substitution of this constraint by a more flexible approach that allows greater use of natural capital could, in all likelihood, raise economic welfare measured across all present and future generations. This is the weak sustainability view.

Many environmental effects are irreversible, for example, the extinction of a species. Irreversibility demands maintenance of the natural capital stock.

It is also suggested that the larger the stock of natural capital, the more resilient an ecosystem is likely to be. (The resilience of an eco-system is judged by its ability to maintain its normal functions often as external disturbance). And the diversity of the eco-system increases its resilience.

The constancy of the stock of natural capital could be interpreted as constancy of all types of natural capital. This means that any use of non-renewable resources would not be compatible with SD.

International Agents and the Environment :

Since 1990 the World Bank and other international agencies have a formulated environment related support programmes, i.e., programmes supporting development, while supporting the importance of the environment in economic development. The WB supports the sustainable development view.

First, it has highlighted the need for assessing all those projects which are expected to generate adverse environmental effects.

Secondly, poverty is found to be the major cause of environmental damage. The reason is that the poor people heavily depend on the environment.

The WTO has recognised the trade-off between trade and the environment and that environmental concerns could lead to protectionism. In spite of this the WTO supports the objective of SD and has been involved in assisting multilateral environmental agreements and increasing the awareness of links between trade and the environment.

The UN Conference on Environment and Development in June 1992—the Rio Earth Summit—leached agreements among 150 countries on reducing global warming by limiting atmospheric emissions by the year 2000 to their 1990 levels.

At the 1997 Kyoto Conference, greenhouse gas emission targets were fixed. The Conference also considered specific programmes to achieve SD in the 21 st century. One of the underlying assumptions of the concept of SD is that poverty is an important cause of environment degradation. It is to this issue that we turn now.

Related Articles:

• Economic Development and Sustainable Development
• Relationship between Economic Growth and Economic Development
• Environment and Economic Development | Economics
• Net National Income and Per Capita Income as Growth Indicators

Home — Essay Samples — Environment — Conservation — Environmental Conservation and Economic Growth

Environmental Conservation and Economic Growth

• Categories: Conservation Economic Growth

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Words: 700 |

Published: Feb 7, 2024

Words: 700 | Pages: 2 | 4 min read

Table of contents

The deductive arguments, evaluating the argument for compatibility, evaluating the argument for conflict, argument for compatibility:.

• Premise 1: Economic growth can provide the resources and technologies necessary for effective environmental conservation.
• Premise 2: Investing in green technologies and sustainable practices can lead to economic opportunities and job creation.
• Premise 3: A growing economy has the capacity to fund environmental protection measures and implement policies that balance economic growth with conservation.
• Conclusion: Therefore, economic growth and environmental conservation are not inherently incompatible and can be pursued together through responsible development and policy measures.

Argument for Conflict:

• Premise 1: The pursuit of economic growth often leads to increased consumption of natural resources and higher levels of pollution.
• Premise 2: Unsustainable economic practices, driven by a focus on growth, can lead to environmental degradation and biodiversity loss.
• Premise 3: Short-term economic gains may come at the expense of long-term environmental well-being.
• Conclusion: Therefore, there is an inherent conflict between unchecked economic growth and effective environmental conservation, requiring a shift in priorities and practices.

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Economic Growth and Development Essay

Differences between economic growth and economic development When compared to economic development as we are to witness shortly, economic growth is a simpler and narrower subject (Acemoglu, 2009). According to the presented definitions, economic growth is a constant and sustained increase in a countries output graded as national.

The increase is depicted on the growth indicators such as education quality of citizens, how healthy a nation is, the technological adaptation rates and quantity of improvement recorded in the way technology is applied (Weil 2005).

The most common way of looking at economic growth is through the sustained improvement in the value of goods and services which are under current production in the contemporary operational environments. When a record increase is made on a country’s GDP, we pronounce an economic growth (Weil 2005).

GDP is the measure used because it shows the sum total of consumption, investments, government expenditure and the net export over a single financial period; they are used for financial forecasting (Friedman 2005).

This mode does not represent an informal size of economy; the rate at which environmental depletion through pollution takes place is likely to discourage growth aspects. Economic development on its part is a numerical indicator which uses numbers in gauging the economic well being of a country through it citizens who are the beneficiaries (Song & Woo 2008).

While economic growth bases its measurements on quantity, economic development is a measure of quality which applies in the context of morality. It is therefore vivid when living standards rise, also when the people realize and increase in their self- esteem and freedom in their operations hence wider options on choices.

Apart from GDP, Human Development Index(HDI) is also a tool used to determine Economic Development since this aspect covers even other factors affecting productivity like literacy levels and life expectancy of a countries population (Nafziger 2006).

Economic development supports economic growth definition and calculation by widening the opportunities in the economy’s productive sectors like health, education, employment and environment since in indicates the per capita income of every individual in a country (Song & Woo 2008).

Economic development busts the living standards through provision of basic requirements of life like health services, shelter, food and education (Nafziger 2006). Therefore, Economic Development is termed to major their concerns on sustainability measures. It is therefore prudent to mention that economic growth is an ingredient required for economic development though it is not needed singly.

Problems concerning measuring economic development and applicable cases Through National Income Accounting an economy maintains a record of its performance to help know how the economy is operating. The limitations of measuring economic development revolve around the setbacks shown by the measures used like the GDP and the HDI.

The measures are less accurate since they only measure Economic Development based on production value as opposed to the actual population; this is irrespective of the fact that the whole population is involved in consumption (McKibben 2007). Economic development describes efficiency on production effectiveness of the population.

If production alone was used in India to measure Economic development, India would today be the most developed country in the whole world since it has the largest GDP (Nafziger 2006). GDP as a measure of growth hence development does not include transactions which are non-market in nature in as much as they are representations of production roles.

This presents a difficulty in valuing production activities that are non- market in nature such as domestic farming in as much as they represent production. Others include cases where individuals do their own laundry and also painting their own houses.

This is applicable for developing countries such as Bangladesh whose economies’ are largely made up of the non-market items hence understatement of their GDP’s with non-market figures. GDP calculation also has the inability of involving adverse effects such as externalities and diseconomies of large-scale production (Stiglitz, Sen & Fitoussi 2010).

Most economies still fails to recognize pollution and environmental hazards which are the greatest components and hence attracts high costs hence posing environmental threat. While the values of negative externalities are incorporated in GDP calculations the costs of the bad side effects are never inculcated resulting into inaccuracies of GDP to show economic development.

Therefore, it is important that the value of externalities be deducted from that of the GDP for it to be relied upon (Stiglitz, Sen & Fitoussi 2010). This is because the proposed replacements such as Net Economic Welfare (NEW) and Measure of Economic Welfare (MEW) failed to pick given the bad effects such as pollution cannot be ignored.

The method of development calculation also fails to clearly differentiate between items produced and the quantity produced, specifically; it does not have the ability of noting all that is produced by an economy (Song & Woo 2008).

This is witnessed in cases where in comparing GDP’s of $480 and $659 of two countries, we would conclude the first country is more developed not knowing that the GDP of $659 is mainly composed of luxurious commodity which is against the spirit of social welfare and therefore unjustifiable Conclusion It is very therefore important to note that there is never a more reliable and more suitable measure that is placed for GDP measurement and translate them to Economic Development.

This therefore leaves us with economic development that is defined in a non-clear way since methods like Human Development index pose a challenge of difficulty in data collection (Okpaji 2008).

Acemoglu, D 2009, Introduction to modern economic growth , Princeton University Press: Princeton.

Friedman, BM 2005, The moral consequences of economic growth , Knopf: New York.

McKibben, B 2007, Deep economy: the wealth of communities and the durable future , Times Books: New York.

Nafziger, EW 2006, Economic development (4th ed.), Cambridge University Press: Cambridge.

Okpaji, A 2008, Economic Progression at our time, Economic Times, 23 (48), 97.

Song, L & Woo, WT 2008, China’s dilemma: economic growth, the environment and climate change , Anu E Press: Canberra.

Stiglitz, JE, Sen, A & Fitoussi, J 2010, Mismeasuring our lives why GDP doesn’t add up , New Press: New York, N.Y.

Weil, DN 2005, Economic growth , Addison-Wesley: Boston.

• Chicago (A-D)
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IvyPanda. (2019, May 20). Economic Growth and Development. https://ivypanda.com/essays/economic-growth-and-development-essay/

"Economic Growth and Development." IvyPanda , 20 May 2019, ivypanda.com/essays/economic-growth-and-development-essay/.

IvyPanda . (2019) 'Economic Growth and Development'. 20 May.

IvyPanda . 2019. "Economic Growth and Development." May 20, 2019. https://ivypanda.com/essays/economic-growth-and-development-essay/.

1. IvyPanda . "Economic Growth and Development." May 20, 2019. https://ivypanda.com/essays/economic-growth-and-development-essay/.

Bibliography

IvyPanda . "Economic Growth and Development." May 20, 2019. https://ivypanda.com/essays/economic-growth-and-development-essay/.

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Test Resources

TOEFL® Resources by Michael Goodine

Toefl writing academic discussion question: economic growth.

This style of question will be used on the redesigned TOEFL iBT starting July 26. Get more samples here .  You can also check out my guide to this task .

The test-taker must read the question posted by the professor and the two student responses.  Finally, they should write their own response which addresses the question and adds to the conversation. They have ten minutes to complete the task.

Your professor is teaching a class on political science. Write a post responding to the professor’s question.  In your response, you should

• express and support your personal opinion
• make a contribution to the discussion in your own words

An effective response will contain at least 100 words. You have ten minutes to write.

Sample Answer 1 (with template)

Here’s a possible response to the above question.  It directly addresses the responses given by the other students.  As you can see, it elaborates on and challenges the arguments they made.  Some test-takers find this approach easiest.

This is a challenging topic, but I think we should prioritize the environment at this time. I strongly agree with Alex’s idea that our lives will be unpleasant if we focus entirely on economic growth. I would add that if the environment is damaged by industrial development we’ll all be more likely to suffer from serious ailments like cancer and lung disease. These sorts of illnesses can be a real strain on our medical systems.  Maggie raised the relevant point that it’s possible that profitable companies will someday solve all of our problems using new technology, but she doesn’t mention that they might arrive far too late to be of use.  For example, it could take decades for an innovative company to create a source of clean energy.

Sample Answer 2 (with template)

Here’s a slightly different approach to the question.  It mostly ignores the responses given by the other students.  Instead, I just focused on my response.  Some test-takers like this approach.

While I appreciate the points mentioned by both Maggie and Alex , I think that we should mainly focus on economic growth.  This is because our lives will become unpleasant if the economy slows down.  We all have bills to pay and those require us to earn an income. Remember that many people have families to take care of, so they need to earn a regular income.  That is only possible if the economy continues to grow at the same rate year after year. Some people may feel that preserving the environment is more important than maintaining our current standard of living, but I tend to disagree. My main focus is on the quality of life that people enjoy at this exact moment, not the lives that future generations will lead.

Need Help Preparing for the TOEFL Writing Section?

To have your practice essays scored and graded by me, check out my essay evaluation service .  I’ll grade your TOEFL essays and correct them line by line.  I’ll even tell you how to do better when you take the real test.

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