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Applied Behavior Analysis

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Basics of a Literature Review

Examples of literature reviews, citation searching.

• Writing and APA Format
• Managing Citations
• Professional Information

Useful Books

• Conducting Research Literature Reviews Pius Library Q180.55.M4 F56 2010
• Preparing Literature Review Qualitative and Quantitative Approaches Pius Library Q180.55.E9 P36 2008 There are several example literature reviews in the appendix of this book.
• Evaluating Research Articles From Start to Finish Pius Library Q180.55.E9 G57 2011
• Evaluating Research Methodology for People Who Need to Read Research Pius Library Q180.55 .E9 D355 2011
• Encyclopedia of Measurement and Statistics eBook
• Encyclopedia of Research Design
• Statistics for People Who Think They Hate Statistics Pius Library HA29 .S2365 2009
• Systematic Reviews in the Social Sciences Pius Library H62.P457 2006
• Systematic Approaches to a Successful Literature Review Pius Library LB1047.3.B66 2012

A literature review may be conducted in order to inform practice and/or policy, serve as a basic element in a thesis or dissertation or as part of a proposal to obtain funding. The process can be divided into a series of steps:

• Choose a topic. Look at recent literature for ideas and do a bit of preliminary searching of the existing literature.
• Clarify your review question and the scope of your review
• Brainstorm search terms to use and think about your search strategy
• Begin searching for articles. I strongly recommend you keep a search log to document which databases you searched and what search terms you used.
• Capture and manage search results. You may want to export results to Endnote or other citation management tool (see Managing Citations tab in this guide)
• Screen results for inclusion based on critera you define
• Evaluate the  the articles. A worksheet which includes the bibliographic information about the article and summarizes elements of the article such as research design, interventions, findings, main variables etc. may give you a helpful overview
• Synthesize results (this is the whole point!).

Literature reviews are part of a PhD dissertation. Use the Dissertations and Theses Full Text database to see the literature review chapters in the two PhD theses listed below. Just enter the dissertation title in quotes and you will retrieve the full text of the dissertation.

• Using concurrent operants to evaluate perserverative conversation in children and adolescents diagnosed with Asperger's disorder by Matthew J. O'Brien
• The effectiveness of specialized applied behavior analysis (ABA) on daily living skills for individuals with autism and related disorders ages 8 to 19 by Adriana Weyandt

• << Previous: Find a specific article
• Next: Writing and APA Format >>
• Last Updated: Mar 12, 2024 12:47 PM
• URL: https://libguides.slu.edu/ABA

Literature Review on Human Behavioural Analysis Using Deep Learning Algorithm

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• First Online: 29 September 2022
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• R. Poorni 19 &
• P. Madhavan 20  

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 654))

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• International Conference on Computational Intelligence in Data Science

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Human behaviour analysis is the active area of research in computer science and engineering which determines the behaviour of humans using various algorithms. The input can be taken from the real time environment to analyze the human predictions. Deep learning plays a vital role as the input data involves a lot of computational images and spatial and temporal information upon which the predictions can be made. In this paper, we discuss the various techniques, concepts and algorithms that are implemented on a various field of image analysis and on real world input data to visualize the behaviour of a human.

• Real time environment
• Deep learning algorithms
• Behaviour analysis

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Tufek, N., Yalcin, M., Altintas, M., Kalaoglu, F., Li, Y., Bahadir, S.K.: Human action recognition using deep learning methods on limited sensory data. IEEE Sens. J. 20 (6), 3101–3112 (2020)

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Mohan, K., Seal, A., Krejcar, O., Yazidi, A.: Facial expression recognition using local gravitational force descriptor-based deep convolution neural networks. IEEE Trans. Instrum. Measure. 70 , 1–12 (2021)

Byeon, Y.-H., Kim, D., Lee, J., Kwak, K.-C.: Explaining the unique behavioral characteristics of elderly and adults based on deep learning. Appl. Sci. 11 , 10979 (2021). https://doi.org/10.3390/app112210979

Angelina, L., Perkowski, M.: Deep learning approach for screening autism spectrum disorder in children with facial images and analysis of ethnoracial factors in model development and application. Brain Sci. 11 , 1446 (2021). https://doi.org/10.3390/brainsci11111446

Bala, B., Kadurka, R.S., Negasa, G.: Recognizing unusual activity with the deep learning perspective in crowd segment. In: Kumar, P., Obaid, A.J., Cengiz, K., Khanna, A., Balas, V.E. (eds.) A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems. ISRL, vol. 210, pp. 171–181. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-76653-5_9

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Department of Computer Science and Engineering, Sri Venkateswara College of Engineering, Pennalur, Sriperumbudur Tk., Tamil Nadu, India

Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India

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Poorni, R., Madhavan, P. (2022). Literature Review on Human Behavioural Analysis Using Deep Learning Algorithm. In: Kalinathan, L., R., P., Kanmani, M., S., M. (eds) Computational Intelligence in Data Science. ICCIDS 2022. IFIP Advances in Information and Communication Technology, vol 654. Springer, Cham. https://doi.org/10.1007/978-3-031-16364-7_25

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DOI : https://doi.org/10.1007/978-3-031-16364-7_25

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Please note you do not have access to teaching notes, influence of website quality on online impulse buying behaviour: a systematic review of literature.

Marketing Intelligence & Planning

ISSN : 0263-4503

Article publication date: 26 March 2024

Online impulsive purchasing is growing exponentially, and website-related factors play a substantial role in this phenomenon. This study provides a comprehensive and integrative framework encompassing a variety of website-related factors influencing impulsive purchase behaviour.

Design/methodology/approach

The study is a systematic literature review, which includes literature search from two prominent databases. This article consolidates the results of 60 relevant research papers, and thematic analysis is performed on various website-related aspects classified into five research topics.

The different website qualities have been classified into broad themes and their role in online impulse buying has been explored. The antecedents, moderators, mediators, and outcomes are portrayed in an integrated research framework. Possible research gaps have been identified, and a future research agenda has been proposed, representing potential research areas.

Research limitations/implications

As we have included only studies published in the English language, this review may be limited by language bias. Relevant research published in other languages might have been excluded.

Practical implications

This literature review may provide management insights to marketers and practitioners managing online retail websites. To sustain an online business in the long term, it is critical for online retailers to have a thorough understanding of all conceivable website stimuli and develop them in a way that compels consumers to make impulsive purchases.

Originality/value

This study represents an original contribution to the realm of systematic literature reviews. To the best of our knowledge, this is the first SLR that elaborately delineates the influence of website-related factors on online impulse buying behaviour.

• Online impulsive purchasing
• Online impulse purchasing behaviour
• Website-related factors
• Systematic literature review
• Website stimuli

Kathuria, A. and Bakshi, A. (2024), "Influence of website quality on online impulse buying behaviour: a systematic review of literature", Marketing Intelligence & Planning , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/MIP-05-2023-0241

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Efficacy of Interventions Based on Applied Behavior Analysis for Autism Spectrum Disorder: A Meta-Analysis

1 Department of Child Rehabilitation, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, China

Weiyi Liang

2 Department of Rehabilitation, Peking University Shenzhen Hospital, Shenzhen, China

To systematically evaluate evidence for the use of interventions based on appied behavior analysis (ABA) to manage various symptoms of children with autism spectrum disorder (ASD).

Sensitivity analyses were conducted by removing any outlying studies and subgroup analyses were performed to compare the effectiveness of ABA and early start denver model (ESDM), picture exchange communication systems (PECS) and discrete trial training (DTT).

14 randomized control trials of 555 participants were included in this meta-analysis. The overall standardized mean difference was d=-0.36 (95% CI -1.31, 0.58; Z=0.75, p=0.45) for autism general symptoms, d=0.11 (95% CI -0.31, 0.54; Z=0.52, p=0.60) for socialization, d=0.30 (95% CI -0.02, 0.61; Z=1.84, p=0.07) for communication and d=-3.52 (95% CI -6.31, -0.72; Z=2.47, p=0.01) for expressive language, d=-0.04 (95% CI -0.44, 0.36; Z=0.20, p=0.84) for receptive language. Those results suggested outcomes of socialization, communication and expressive language may be promising targets for ABA-based interventions involving children with ASD. However, significant effects for the outcomes of autism general symptoms, receptive language, adaptive behavior, daily living skills, IQ, verbal IQ, nenverbal IQ, restricted and repetitive behavior, motor and cognition were not observed.

The small number of studies included in the present study limited the ability to make inferences when comparing ABA, ESDM, PECS and DTT interventions for children with ASD.

INTRODUCTION

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by early impairments in socialization and communication, as well as restricted interests and repetitive behaviors [ 1 ]. Currently, the Centers for Disease Control and Prevention (CDC) estimates that one in every 59 children has ASD [ 2 ]. Although most children are diagnosed at the age of 3 years old, approximately 39% are not evaluated for the first time until after 4 years old [ 2 ].

ASD is recognized as a major public health concern because of its early onset, long duration, and high levels of associated impairments [ 3 ]. This impairment is attributable not only to the core symptoms of ASD, but also to a range of co-existing conditions that individuals with ASD often experience, including emotional and behavioral problems (i.e., anxiety, compulsions, aggression destruction and uncooperative behavior), sleep problems (i.e., difficulty in falling asleep, superficial sleep, early awakening and low sleep efficiency), feeding and eating problems, gastrointestinal problems, sensory sensitivities, learning and intellectual disabilities, as well as comorbid health and mental health diagnoses [ 4 ]. Compared with the core features of ASD, these co-existing conditions can be equal or greater for parents and teachers of children with ASD than the core, and have a significant impact on behavior management, learning acquisition, and the development of social relationships [ 5 ]. There are many intervention approaches for treating ASD, including applied behavior analysis (ABA), diets and vitamins, floor time, holding, medication, sensory integration, speech and music therapy, special education and visual schedules [ 6 , 7 ]. However, there is little empirical evidence to prove the effectiveness of these approaches and the available evidence shows mixed results [ 8 - 11 ].

ABA is a scientific approach in which procedures based on the principles of behavior are systematically applied to identify environmental variables that influence socially significant behavior and are used to develop individualized and practical interventions [ 12 , 13 ]. This methodology is highly effective in teaching basic communication, games, sports, social interaction, daily living and self-help skills. As the increasing number of service providers and certified professionals in the field have suggested, the ABA field has shown even more significant growth in the field of behavioral interventions for children with autism [ 14 , 15 ]. Since the mid-1980s, there has been evidence that ABA has contributed to the steady accumulation of intelligence, language and social functions in children with ASD [ 16 - 18 ].

Nowadays, there are also several types of interventions which are based on ABA and share a common set of core features, such as Early Start Denver Model (ESDM), Picture Exchange Communication Systems (PECS), Discrete Trial Training (DTT) and Pivotal Response Treatment (PRT). ESDM uses the teaching strategies which involve interpersonal exchange and positive affect, shared engagement with real-life materials and activities, adult responsivity and sensitivity to child cues, and focus on verbal and nonverbal communication, based on a developmentally informed curriculum that addresses all developmental domains [ 19 ].

PECS is a manualized program that guides children to use an exchange-based communication system, which has been a common intervention choice for nonverbal children with ASD in clinical and school settings [ 20 ]. DTT consists of a series of direct and systematic instruction methods, which are used repeatedly until the children acquires the skills and focuses on analyzing the skills into small elements and units [ 21 ]. PRT is an intervention that focuses on arranging the environment to promote the use of target structures and then provides opportunities for children to use the target structures in natural game interactions [ 22 ]. Even though these interventions have their own designs and performance forms, they are all consistent with the principles of ABA and show effectiveness in different functions of children with ASD [ 19 - 22 ].

The literature on ABA-based interventions for children with ASD has been constantly growing over the past decade. At present, there are quite a number of studies on psychosocial interventions based on ABA in children with ASD. Furthermore, comparable outcome measures were used in the study to make meta-analysis possible. This meta-analysis would include ABA-based interventions like ABA, ESDM, PECS, DTT, PRT and so on.

The primary purpose of this meta-analysis was to systematically review the evidence for the use of ABA-based interventions to manage dysfunction in children with ASD. In addition, we would also examine the differences among types of ABA-based interventions in the improvement of ASD symptoms.

Protocol registration and PRISMA guidelines

The procedures for this meta-analysis have been registered in the PROSPERO International prospective register of systematic reviews (No. CRD42018118487), which published protocols from systematic reviews prior to the initiation of data extraction in an effort to reduce reporting bias [ 23 , 24 ]. The methods used to conduct this study were in accordance with the Cochrane Handbook for Systematic Reviews [ 25 ]. This study was designed in accordance with the PRISMA guidelines [ 26 ].

Information sources and search strategy

Two independent researchers identified studies by searching electronic databases and manually searching for appropriate published studies and published system reviews. The following databases were searched: Pubmed, Embase, Web of Science, Cochrane Library, Wanfang and Weipu. The main keywords utilized in the article searches included the following: autism spectrum disorder, autism, autistic disorder, ASD; applied behavior analysis, ABA; discrete trial teaching, DTT; pivotal response treatment, PRT; picture exchange communication system, PECS; early start denver model, ESDM; psychotherap* and cognitive behavi* therap*. It was limited to the title, abstract or topic, depending on the availability of search options in each database. The search was limited to journals in English and Chinese. Additionally, the search was not limited by date. Thus, all databases were searched from the earliest indexed date to December 24, 2018.

Eligibility criteria and study selection

Studies were included if they met the following criteria: 1) the study should be a randomized controlled trial (RCT); 2) participants were between the ages of 0 and 18 years old; 3) participants were diagnosed with ASD; 4) the treatment used in experimental group was based on / derived from applied behavior analysis (DTT, PST, PECS, ESDM and so on); 5) the treatment used in control group was conventional intervention; 6) the study included at least one standardized continuous outcome measure related to autistic symptom. The final selection of studies was performed using tools provided in the Cochrane Collaboration Handbook [ 27 ].

Selection of outcome measures

Outcome measures were selected depending on their validity and frequency of use. Judgement of the validity of autistic symptoms measures in the ASD population was based on two methodologically rigorous reviews which were recently published. This study mainly selected outcomes related to high-frequency autistic symptoms (used more than 3 times in all included researches). Therefore, the general symptomatic outcomes of ASD, including socialization outcomes, communication outcomes, expressive language outcomes, receptive language outcomes, adaptive behavior outcomes, daily living skills outcomes and intelligence quotient (IQ) outcomes, were finally selected in this study. The selected indicators of general symptom outcomes for ASD were Mullen Scales of Early Learning (MSEL), Autism Diagnostic Observation Schedule (ADOS), Assessment of Basic Language and Learning Skills (ABLLS), Aberrant Behavior Checklist (ABC), The Autism Diagnostic Interview-Revised (ADI-R), Vineland Adaptive Behavior Scales (VABS), Autism Treatment Evaluation Checklist (ATEC) and Childhood Autism Rating Scale (CARS). The selected measures for socialization outcome were ADI-R and VABS. The selected measures for communication outcome were VABS and Psychoeducational Profile (C-PEP). The selected measures for expressive language outcome were MSEL, ADOS and Reynell Developmental Language Scales (RDLS). The selected measures for receptive language outcome were RDLS and MSEL. The selected measures for adaptive behavior outcome were VABS and C-PEP. VABS was also used for daily living skills outcome measure. In addition, Differential Ability Scales (DAS) and Stanford-Binet Intelligence Scale (SBIS) were chosen as measures of IQ outcomes.

If two of the selected outcome measures were used in a study, one of them was chosen for analysis.

Data collection process and risk of bias within studies

Data extraction and risk of bias assessment were performed according to the Cochrane Collaboration Guidelines. All references found by the search strategy were gathered by the reference management program EndNote X6 (Thomson Reuters, New York City, USA). All citation sourced from the search strategy were transferred to EndNote X6. The first author conducted the systematic search and the second author verified inclusion/exclusion of a subset of studies. The two authors independently screened the originally selected studies and agreed on which studies should be selected for the review. Data extraction and risk of bias assessment were conducted independently by the first and second authors. In the event of a disagreement, resolutions were reached in discussion with the third referees, if necessary following inspection of the full paper. The Cochrane Collaborative tool was used to assess the risk of bias in each included study. The tool included the following domains: sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, complete outcome data, selective outcome reporting, and other sources of bias. Studies were allocated to three categories according to our judgment of each area or potential risk of bias: low risk of bias, unclear risk of bias and high risk of bias. Only methodological strengths and weaknesses associated with the results of this meta-analysis were considered when assessing the risk of bias. Whether the study should be included in the meta-analysis is judged individually based on the results of the risk of bias assessments, excluding those with higher bias risk.

Selection bias was assessed based on adequate description of random sequence generation and concealment of treatment group allocation. In order to maintain the highest level of scientific and methodological rigor, it was determined that only RCTs would be included in this review. Thus, selection bias would only come from treatment allocation. Due to the nature of interventions, blinding of participants and personnel was not feasible in any of the included studies. Thus, all studies had a high risk of performance bias. Attrition bias was assessed by examining the reports of withdrawals and drop-outs. Outcome data were considered complete if there were no missing pre- or post-treatment data, or if the study authors had carried out an intent-to-treat analysis. Therefore, reporting bias was evaluated purely based on evidence of selective outcome reporting provided in the study reports. There was no exclusion study based on bias risk assessment.

Summary measures and syntheses of results

Data syntheses were performed using Review Manager version 5.3 (Cochrane Collaboration software). We assessed continuous data and analyzed continuous data based on the basis of the available means and standard deviations. There was no clear evidence that the distribution was biased. Assuming that two or more studies were found to be suitable for inclusion and that those studies were considered to be satisfactory, a meta-analysis of the results was performed. Since the studies measured several outcomes in a nonuniform manner, outcome data were synthesized using standardized mean difference (mean/standard deviation) for both intervention and control group.

Higgin’s I 2 test was used to describe the impact of heterogeneity on the effect estimates in percentage terms. It was chosen over Cochrane’s Q Test (a value of 0.10 used as a cut-off for significance) since the latter had low power when there were few studies. Higgins et al. [ 28 ] proposed a tentative classification of I 2 values with the purpose of helping to interpret its magnitude. Thus, percentages of around 25% (I 2 =25), 50% (I 2 =50), and 75% (I 2 =75) would mean low, medium, and high heterogeneity, respectively. And a random-effect model was chosen to estimate the effect of intervention [ 29 ].

Additional analysis

Due to the relatively limited research addressing treatment options based on ABA for children and adolescents with ASD, it was deemed appropriate to include studies that used applied behavior analysis (ABA), discrete trial teaching (DTT), pivotal response treatment (PRT), picture exchange communication system (PECS) or early start denver model (ESDM) as intervention in experimental group. To compare the effectiveness of these delivery methods, a subgroup analysis was conducted by comparing the confidence intervals of the summary estimates in these subgroups (ABA group, DTT group, PRT group, PECS group, and ESDM group). No overlap or minimal overlap between the confidence intervals was considered statistically significant. Only subgroup analysis of the result measurements was performed if the overall summary estimates were significant.

Because of the small number of studies in each review category, it is not possible to formally assess publication bias through funnel plots or statistical tests [ 30 , 31 ]. In order to analyze the impact of outlying studies on summary estimates, sensitivity analysis was carried out by removing each type of outlier studies.

Study selection

Flow diagram of the search results was shown in Figure 1 . 1,421 records were identified through database searching and 2 of additional records were identified from published systematic review. After removing 306 duplicated records, 1,117 records were screened based on the title and abstract, 1,242 of which were excluded. 33 of full-text articles were assessed for eligibility and 19 of them were excluded for the following reasons: eight studies were not RCTs; one study could not provide full text after contacting the author; three studies only provided abstracts of conference articles; four studies did not meet the requirement for participants; three studies did not meet the requirement of interventions in the control group; one study did not include relevant outcomes. Finally, 14 RCTs were included in this review and meta-analysis [ 32 - 45 ].

PRISMA flow diagram of study selection.

Study characteristics

A summary of study characteristics could be found in Table 1 . A total of 555 participants (278 of experimental groups and 277 of control group) aged 6 to 102 months were included. Participants were composed of American, European, Latino, Asian, African and multiracial people. All participants in 14 studies had diagnosis of ASD by clinicians with the ADOS, the Autism Diagnostic Interview-Revised (ADI-R), the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-VI) or the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) [ 32 - 45 ]. Each study included at least one standardized continuous outcome measure related to autistic symptoms, such as socialization, communication, adaptive behavior, language, verbal IQ, non-verbal IQ, inappropriate speech, response, imitation, irritability, noncompliance, motor, body use, activity level, daily living skills, self-help, IQ, cognitive, early-learning, visual reception, general impression and so on. The scales with higher using frequency were ADI-R, ADOS, MSEL, VABS, ABC, CARS, ABLLS, ATEC and C-PEP.

A summary of study characteristics

Exp.: experimental group, Cont.: control group, ADI-R: Autism Diagnostic Interview-Revised, ADOS: Autism Diagnostic Observation Schedule, MSEL: Mullen Scales of Early Learning, PDD-BI: Pervasive Developmental Disorder-Behavioral Inventory, VABS: Vineland Adaptive Behavior Scales, RBS: Repetitive Behavior Scale, DAS: Differential Ability Scales, ABC: Aberrant Behavior Checklist, SBIS-4: Stanford-Binet Intelligence Scale-Fourth Edition, LIPS-R: Leiter International Performance Scale-Revised, ABLLS: Assessment of Basic Language and Learning Skills, ABLLS-R: Assessment of Basic Language and Learning Skills-Revised, NVCSS: Non-verbal Communication Skills Scale, GARS-2: Gilliam Autism Rating Scale-Second Edition, SSIS: Social Skills Improvement System, SRS: Social Responsiveness Scale, WM: Walker-McConnell Scale of Social Competence and School Adjustment, SVS: Social Validity Survey, CARS: Childhood Autism Rating Scale, CGI-S: Clinical Global Impression-Severity, RDLS: Reynell Developmental Language Scales, ACBC: Achenbach Child Behavior Checklist, WIAT: Wechsler Individualized Achievement Test, ELM: Early Learning Measure, FSQ: Family Satisfaction Questionnaire, ATEC: Autism Treatment Evaluation Checklist, C-PEP: Psychoeducational Profile, ASSS: Autism Social Skills Scale, PedsQLTM: Pediatric Quality of Life Inventory Measurement Model

Five studies used ABA-based intervention [ 36 , 38 , 40 - 42 ], one study used DTT [ 37 ], five studies used ESDM [ 32 - 34 , 39 , 43 ] and three studies were found to use PECS [ 35 , 44 , 45 ]. Eight studies were administrated by trained therapists [ 32 , 33 , 36 , 40 - 42 , 44 , 45 ], while five by teachers [ 35 , 37 - 39 , 43 ] and one by parents [ 34 ]. Seven studies encouraged parents or caregivers to assist with generalization of acquired skills to the home environment and one of them also needed parents or caregivers to cooperate with therapists on home visit and supervision [ 32 , 33 , 36 , 39 - 41 , 44 ]. Dawson et al. [ 33 ] provided continuous training for parents during semimonthly meeting to help them use the ESDM strategy in their daily activities [ 32 ]. The duration of each session was 30 to 120 minutes and the duration of the intervention was between 2 and 36 months. Intervention settings varied in different studies, such as center, elementary school, mainstream school, institution, kindergarten, department of developmental-behavioral pediatrics in hospital and home. All studies were approved by local Institutional Review Board and informed consents were obtained from the participants’ parents. Gordon et al. [ 35 ] assigned all participants into three intervention groups. The patients in immediate treatment group (ITG; five class groups, 26 children) received training immediately after baseline assessment. The patients in delayed treatment group (DTG; six class groups, 30 children) received training about 9 months later and immediately after the second assessment. The patients in no-treatment group (NTG; six class groups, 28 children) received no training. In this review, we only selected the patients in immediate treatment group and no-treatment group. A summary of study characteristics can be found in Table 1 .

Risk of bias within studies

Selection bias (random sequence generation and allocation concealment).

All of the included studies were performed with adequate random sequence generation, either manually generated or computer-generated. Dawson et al. [ 32 ] used random permuted blocks (Fourth Edition), while Li et al., [ 39 ] Yan et al. [ 42 ] and Kong et al. [ 44 ] used randomized digital table. Dawson et al., [ 32 ] Gordon et al., [ 35 ] Hamdan et al., [ 37 ] Leaf et al., [ 38 ] Li et al., [ 39 ] Sallows et al., [ 40 ] Smith et al., [ 41 ] Yan et al., [ 42 ] Xu et al. [ 43 ] and Kong et al. [ 44 ] performed adequate allocation concealment. The remainder of the included studies indicated that allocation concealment was implemented, but did not provide sufficient information about the concealment method.

Performance and detection bias (blinding of participants, personnel and outcome assessment)

As previously stated, blinding of participants and personnel was not possible in any of the included studies. And in all studies, clinicians rating scales were blind to treatment allocation so these outcome measures were considered to have a low risk of detection bias.

Attrition and reporting bias (incomplete outcome data and selective outcome reporting)

Dawson et al. [ 32 ] was considered to have a high risk of attrition bias due to the deletion of missing data from the study analysis. The remainder of the included studies were deemed to have complete outcome data. There was no evidence of selective outcome reporting in any of the studies included. Risk of bias within studies is shown in Figure 2 .

Risk of bias within studies.

Outcome of general symptoms of ASD

Eleven studies reported the outcome of general symptoms of ASD and we rated the overall quality of the evidence as moderate [ 34 - 36 , 38 - 45 ]. These studies reported 434 participants (215 in the experimental condition and 219 in the control condition). The overall standardized mean difference (SMD) was d=-0.36 (95% CI -1.31, 0.58; Z=0.75, p=0.45) with no significant difference between the experimental and control conditions. There were high levels of heterogeneity across included studies (I 2 =94%). A forest plot illustrating these results was included in Figure 3 .

Forest plots.

A subgroup analysis was carried out on ABA-based intervention [ 36 , 38 , 40 - 42 ] versus PECS intervention [ 35 , 44 , 45 ] versus ESDM intervention [ 34 , 39 , 43 ] to compared the outcome of general symptoms of ASD. There was no significant difference in the effectiveness of interventions among ABA subgroup, ESDM subgroup and ESDM subgroup. In the ABA-based intervention subgroup, the overall SMD was d=-0.12 (95% CI -1.34, 1.10; Z=0.19, p=0.85) with on significant difference between experimental and control conditions [ 36 , 38 , 40 - 42 ]. As the high levels of heterogeneity existed, we found that the SDM reported by Sallows et al. [ 40 ] was an outlier. Therefore, we carried out a sensitivity analysis by removing the study of Sallows et al. [ 40 ] So that the overall SMD in ABA-based intervention subgroup was changed to 0.67 (95% CI -0.06, 1.29; Z=2.14, p=0.03) and the difference between intervention and control conditions was significant.

In the PECS intervention subgroup, the overall SMD was d=-3.67 (95% CI -7.88, 0.54; Z=1.71, p=0.09) [ 35 , 44 , 45 ]. There were no significant differences between experimental and control conditions and the levels of heterogeneity among studies were high (I 2 =98%). After conducting sensitivity analysis by removing the studies of Gordon et al., [ 35 ] Liu et al. [ 45 ] and Kong et al. [ 44 ] respectively, the insignificance between experimental and control groups remained (p=0.16; p=0.32; p=0.33).

In the ESDM intervention subgroup, the overall SMD was d=-0.55 (95% CI -0.92, -0.17; Z=2.86, p=0.04) with significant difference between the experimental and control conditions [ 34 , 39 , 43 ]. There was no significant heterogeneity across studies (I 2 =0%, p=0.76).

Outcome of socialization

Six studies reported the outcome of socialization and we rated the overall quality of the evidence as moderate [ 32 - 34 , 36 , 40 , 41 ]. These studies reported 200 participants (101 in the experimental condition and 99 in the control condition). The overall SMD was d=0.11 (95% CI -0.31, 0.54; Z=0.52, p=0.60) and there were moderate levels of heterogeneity across studies (I 2 =55%, p=0.05). Since there was significant baseline imbalance [Mean (SD): Experimental group=-0.8 (4.7), Control group=3.4 (5.7); d=-0.80, 95% CI -1.41, -0.18] in the study of Dawson et al., [ 32 ] we performed sensitivity analysis by removing this study. The sensitivity analysis altered the results in terms of statistical significance between experimental group and control group (Z=2.01, p=0.04) and heterogeneity among studies (I 2 =0%, p=0.94). A forest plot illustrating these results was included in Figure 3 .

Subgroup analysis was also conducted to compare ABA-based intervention [ 36 , 40 , 41 ] and ESDM intervention [ 32 - 34 ]. It was noted that there was no study used PECS or DTT intervention to report outcomes of socialization. There was no significance in the effectiveness of interventions between ABA subgroup and ESDM subgroup. In the ABA-based intervention subgroup, there was no significant difference (p=0.60) between experimental and control groups and its heterogeneity was insignificant (I 2 =0%, p=0.81) [ 36 , 40 , 41 ]. In the ESDM intervention subgroup, there was still no significance (p=0.90) between experimental and control conditions and there were moderate levels of heterogeneity across studies (I 2 =78%, p=0.01) [ 32 - 34 ]. Although the study of Dawson et al. [ 32 ] was regarded as an outlier and was removed to conduct a sensitivity analysis, the insignificance between experimental and control conditions remained (p=0.13) with no significant heterogeneity among studies (I 2 =0%, p=0.54).

Outcome of communication

Seven studies reported the outcome of communication and we rated the overall quality of evidence as moderate [ 33 , 34 , 36 , 40 , 41 , 44 , 45 ]. These studies reported 246 participants (122 in the experimental condition and 124 in the control condition). The overall SMD was d=0.30 (95% CI -0.02, 0.61; Z=1.84, p=0.07) with no significance between experimental and control conditions. There were low levels of heterogeneity across studies (I 2 =33%, p=0.18).

Since Estes et al.’s [ 34 ] study had significant baseline imbalance [Mean (SD): the experimental group=5.3 (20.2), the control group=10 (17.2); d=-0.24, 95% CI -0.92, 0.43], we performed a sensitivity analysis by removing Estes et al.’s [ 34 ] study. The heterogeneity among studies decreased (I 2 =16%, p=0.31) and the difference between experimental and control groups changed to be significant.

Among the seven studies that reported outcome of communication, three used ABA-based intervention [ 36 , 40 , 41 ], two used ESDM [ 33 , 34 ] and two used PECS [ 44 , 45 ]. A subgroup analysis to compare ABA versus ESDM versus PECS interventions was conducted and differences among subgroups were insignificant (p=1.00). In ABA, ESDM and PECS subgroups, the differences between experimental and control groups were all insignificant (p=0.31, p=0.16, p=0.07). A forest plot illustrating these results was included in Figure 3 .

Outcome of expressive language

Four studies reported the outcome of expressive language and we rated the overall quality of evidence as moderate [ 33 , 35 , 40 , 41 ]. These studies reported 150 participants (78 in the experimental condition and 72 in the control condition). Among the four studies, two used ABA-based intervention [ 40 , 41 ], one used ESDM33 and one used PECS [ 35 ]. Thus, we did not have adequate studies to carry out a subgroup analysis. Significant improvement was shown in the overall synthesis (p=0.01). The heterogeneity was significant (p<0.00001, I 2 =97%). Since Gordon et al.’s [ 35 ] study had significant baseline imbalance [Mean (SD): experimental group=0.9 (0.2), control group=7.5 (0.1); d=-41.62, 95% CI -49.79, -33.45], we performed sensitivity analysis by removing it. The sensitivity analysis altered the result of heterogeneity (I 2 =0%, p=0.39). A forest plot illustrating these results was included in Figure 3 .

Outcome of receptive language

Three studies reported the outcome of receptive language and we rated the overall quality of evidence as moderate [ 33 , 40 , 41 ]. These studies reported 96 participants (52 in the experimental condition and 44 in the control condition). Two of these studies chose ABA-based intervention [ 40 , 41 ] and one chose ESDM intervention [ 33 ]. There was not significant heterogeneity across studies (I 2 =0%, p=0.52) and we did not find significant effectiveness in the overall synthesis (p=0.84). A forest plot illustrating these results was included in Figure 3 .

Outcome of adaptive behavior

Six studies reported the outcome of adaptive behavior and we rated the overall quality of evidence as moderate [ 34 , 40 , 41 , 43 - 45 ]. These studies reported 210 participants (106 in the experimental condition and 104 in the control condition). Among the six studies, two used ABA-based intervention [ 40 , 41 ], two used ESDM [ 34 , 43 ] and two used PECS [ 44 , 45 ]. The overall synthesis indicated insignificance between experimental and control conditions (p=0.93) with insignificant heterogeneity (I 2 =0%, p=0.70). A subgroup analysis was conducted to compare the effectiveness of ABA, ESDM and PECS and it showed that there were no significant differences between experimental and control conditions in each subgroup (p=0.78, p=0.29, p=0.39). A forest plot illustrating these results was included in Figure 3 .

Outcome of daily living skills

Five studies reported the outcome of daily living skills and we rated the overall quality of evidence as moderate [ 33 , 34 , 36 , 40 , 41 ]. These studies reported 77 participants (36 in the experimental condition and 41 in the control condition). Among the five studies, three used ABA-based intervention [ 36 , 40 , 41 ] and two used ESDM [ 33 , 34 ]. The overall SMD was d=0.31 (95% CI -0.22, 0.84; Z=1.14, p=0.26) with no significant difference between the experimental and control conditions. As there were moderate levels of heterogeneity among studies and Dawson et al.’s [ 33 ] study showed significant baseline imbalance [Mean (SD): experimental group=-22.6 (11.9), control group=-28.8 (9.2)], we conduct a sensitivity analysis by removing it. It was noted that the heterogeneity among studies decreased (I 2 =18%) and the differences between experimental and control groups remained insignificant (p=0.57). The subgroup analysis was used to compare the effectiveness of ABA and ESDM, and there were no significant differences between experimental and control conditions in each subgroup (p=0.43, p=0.47). A forest plot illustrating these results was included in Figure 3 .

Outcome of IQ

Four studies reported the outcome of IQ and we rated the overall quality of evidence as moderate [ 34 , 36 , 40 , 41 ]. These studies reported 116 participants (57 in the experimental condition and 59 in the control condition). Three of these studies chose ABA-based intervention [ 36 , 40 , 41 ] and one chose ESDM intervention [ 34 ]. The heterogeneity test showed insignificant differences across studies (I 2 =0%, p=0.78) and no significant effectiveness were found between experimental and control conditions in the overall synthesis (p=0.87). A forest plot illustrating these results was included in Figure 3 .

Other outcomes

In terms of verbal IQ, nonverbal IQ, restricted and repetitive behavior, and motor and cognition, there was no significant difference in the effectiveness of interventions between experimental and control conditions. We did not carry on the following analysis because only two studies reported in each outcome measure (p=0.56, p=0.65, p=0.30, p=0.32, p=0.57). For the outcomes that only one study recorded, we did not conduct any test with inadequate studies.

We performed a meta-analysis of ABA-based studies (ABA, ESDM, PECS and DTT) in this study to investigate the overall effectiveness of the intervention programmers for children with ADS, and we observed no significant effects for the outcomes of general symptoms of ASD, receptive language, adaptive behavior, daily living skills, IQ, verbal IQ, nonverbal IQ, restricted and repetitive behavior, motor and cognition. However, significant effects were shown on socialization, communication and expressive language.

This study compared three types of ABA-based interventions (ABA, ESDM and PECS). Only one study reported relevant outcomes of DTT so that we could not include it in subgroup analysis. In ABA-based intervention subgroup and ESDM intervention subgroup, there were significant differences in the effectiveness between experimental and control conditions while PECS intervention not. ABA and ESDM did not have significant differences in the effectiveness on socialization and daily living skills. Additionally, all of ABA, ESDM, and PECS had no significant differences in effects on communication and adaptive behavior. As for other outcomes, there were not available studies to include in the analyses.

This study conducted a meta-analysis of ABA-based interventions (ABA, ESDM, PECS, and DTT) for children with ASD. Although several meta-analyses have assessed intervention programs related to ABA, all of them only chose one type of ABA-based interventions that could not comprehensively reflect the effectiveness and some of them included non-randomized controlled trials so that it would introduce significant bias in the data analysis. Moreover, the quality of evidence for all outcomes were moderate, resulting in more reliable evidence than that produced by previous studies.

The small number of available studies has been limited in the ability to make inferences in comparing the four types of ABA-based interventions and investigating each type of intervention’s strengths and weaknesses in terms of important outcomes. This review also neglected the influences of participant baseline levels and parent participation.

Regarding the outcome of autism general symptoms of ASD in this study, we concluded that there was not enough evidence to support the effectiveness of ABA-based interventions for treating ASD. However, the results of subgroup analysis suggested that there was possibility of effectiveness in ABA subgroup and ESDM subgroup. A previous systematic review also showed the similar result [ 46 ]. In the previous review, three types of interventions were targeted: 1) behavioral interventions-based essentially on learning theory and on ABA (limited to not only early intensive behavioral intervention, but also included ABA programs derived from it; 2) social-communication focused interventions, targeting social communication impairment, as the core symptom of ASD; 3) multimodal developmental interventions targeting a comprehensive range of children’s development. In the subgroup analysis, the behavioral intervention subgroup included two studies that chose ABA-based interventions and suggested that there was not enough evidence to support the treatment effectiveness of ABA-based interventions [ 47 , 48 ]. One of the two studies was included in our meta-analysis [ 41 ] and the other study was not included because its participants had no definite diagnosis of ASD. Even though both the previous study and our study have consistent conclusion, further study is still needed to accumulate evidence on the effect on general symptoms of ASD because of limited researches.

Regarding the outcomes of socialization, communication and expressive language in this study, we concluded that there was significant effectiveness of ABA-based interventions. The results on daily living skills did not show significant effectiveness of ABA-based interventions on this outcome. The results of Makrygianni et al.’s [ 49 ] study were consistent with our study: ABA-based interventions were moderately to very effective in improving communication skills (Effect Size: g=0.650) and expressive language skills (Effect Size: g=0.742), moderately effective in improving socialization (Effect Size: g=0.444), lowly effective in improving daily living skills (Effect Size: g=0.138). The effect size (ES) in previous studies was one of the indexes of magnitude and direction of the treatment effect [ 50 - 52 ]. Specifically, ES constituted a quantitative assessment of the magnitude and the power of a phenomenon [ 53 ]. The type of ES, used in the present study, was the standardized mean change (ESchange) which expressed the difference between pre- and post-treatment measures. Hedges [ 54 ] g was used to calculate the standardized mean change because it constituted a conservative estimate. For the interpretation of ES, Cohen provided Rules-of-Thumb suggesting that 0.2 represented a small ES, 0.5 represented a medium ES and 0.8 represented a large ES [ 51 ].

Studies used quasi-experimental, within-subjects, and prepost design to evaluate the efficacy of ABA-based interventions on ASD. The remaining studies used a quasi-experimental between-groups pre-post design, comparing the performance of an experimental group, receiving ABA-based intervention while a control group received an eclectic or “treatment-asusual” intervention. Only two studies used a random experimental between-groups pre-post design and were included in our meta-analysis [ 40 , 41 ]. The outcomes in some studies were based on an assessment of larger number of studies and used more rigorous analyses to estimate mean effect sizes of each outcome. However, due to the selection bias of the included studies, larger sample randomized control trials are still needed. For the outcomes of IQ, verbal IQ, nonverbal IQ, restricted and repetitive behavior and motor, we did not find relevant studies to compare and analyze.

The present study also demonstrated the insignificant effectiveness of ABA-based interventions for children with ASD on receptive language, adaptive behavior and cognition, which was consistent with the previous study [ 55 ]. In the previous study, thirteen studies met the inclusion criteria and six of them were randomized comparison trials with adequate methodologic quality. Meta-analysis of 4 studies concluded it when compared with standard care. ABA intervention programs did not significantly improve the cognitive outcomes of children in the experimental group who scored a SMD of 0.38 (95% CI 0.09 to 0.84; p=0.11), for receptive language; SMD of 0.29 (95% CI 0.17 to 0.74; p=0.22) or adaptive behavior; SMD of 0.39 (95% CI 0.16 to 0.77; p=0.20). Among the four included studies, two studies were not eligible for this review because they excluded children with ASD who had an IQ score less than 50 [ 56 , 57 ] and the others were included in this review [ 40 , 41 ]. Thus, currently, there is inadequate evidence that ABA-based interventions have better outcomes than standard care for children with ASD on receptive language, adaptive behavior and cognition.

Additionally, we found that long-term, comprehensive ABA-based interventions were beneficial to lifelong development of children with ASD. In Virués-Ortega’s study, the results suggested that long-term, comprehensive ABA-based intervention led to (positive) medium to large effects in terms of intellectual functioning, language development, acquisition of daily living skills and social functioning in children with ASD [ 58 ]. Although favorable effects were apparent across all outcomes, language-related outcomes (IQ, receptive and expressive language, communication) were superior to nonverbal IQ, social functioning and daily living skills, with effect sizes approaching 1.5 for receptive and expressive language and communication skills. Dose-dependent effect sizes were apparent by levels of total treatment hours for language and adaptation composite scores. In Roth et al.’s [ 59 ] study, adolescents and adults with ASD were included and the results suggested that the behavioral interventions in the areas of academic skills, adaptive skills, problem behavior interventions in the areas of academic skills, adaptive skills, problem behavior, phobic avoidance, social skills, and vocational skills had medium-to-strong effect sizes. Medium-to-high confidence in findings was noted for 81% of the studies in the meta-analysis; however, three-fourths of the reviewed studies did not include treatment integrity, which may affect the ability to draw conclusion about the effectiveness of the interventions. Therefore, it is necessary for children with ASD to ensure long-term adherence to treatment, for ABA-based interventions may have slower effect.

It was also noted that parental synchrony and sensitivity played a role in helping mediators enhance the communication and social interaction of children with ASD [ 60 ] and in the effectiveness of enhancing children’s reciprocity of social interaction toward others not only in Aldred et al. [ 61 ] and Green et al., [ 62 ] but also in the other studies [ 63 - 67 ]. However, our study did not consider the influences of parental synchrony and sensitivity, which should be improved in the further study.

This review suggested that the outcomes of socialization, communication and expressive language may be promising targets for ABA-based interventions involving children with ASD. However, significant effects for the outcomes of general symptoms of ASD, receptive language, adaptive behavior, daily living skills, IQ, verbal IQ, nonverbal IQ, restricted and repetitive behavior, motor and cognition were not observed. The small number of studies included in the present study were limited in the ability to make inferences when comparing ABA, ESDM, PECS and DTT interventions for children with ASD and investigating the strengths and weaknesses of each type of intervention in terms of important outcomes. More methodologically rigorous researches will be necessary to ascertain the precise potential of ABA-based interventions for children with ASD.

Acknowledgments

This study was supported by the Joint Construction Project of Henan Medical Science and Technology Research Plan (No.2018020223).

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: Qian Yu. Data curation: Enyao Li, Liguo Li, Weiyi Liang. Formal analysis: Weiyi Liang. Investigation: Weiyi Liang. Methodology: Qian Yu. Project administration: Qian Yu. Resources: Qian Yu. Software: Weiyi Liang. Supervision: Qian Yu. Validation: Qian Yu. Visualization: Qian Yu. Writing—original draft: Weiyi Liang, Qian Yu. Writing—review & editing: Qian Yu.

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• Published: 21 March 2024

Expert review of the science underlying nature-based climate solutions

• B. Buma   ORCID: orcid.org/0000-0003-2402-7737 1 , 2   na1 ,
• D. R. Gordon   ORCID: orcid.org/0000-0001-6398-2345 1 , 3   na1 ,
• K. M. Kleisner 1 ,
• A. Bartuska 1 , 4 ,
• A. Bidlack 5 ,
• R. DeFries   ORCID: orcid.org/0000-0002-3332-4621 6 ,
• P. Ellis   ORCID: orcid.org/0000-0001-7933-8298 7 ,
• P. Friedlingstein   ORCID: orcid.org/0000-0003-3309-4739 8 , 9 ,
• S. Metzger 10   nAff15   nAff16 ,
• G. Morgan 11 ,
• K. Novick   ORCID: orcid.org/0000-0002-8431-0879 12 ,
• J. N. Sanchirico 13 ,
• J. R. Collins   ORCID: orcid.org/0000-0002-5705-9682 1 , 14 ,
• A. J. Eagle   ORCID: orcid.org/0000-0003-0841-2379 1 ,
• R. Fujita 1 ,
• E. Holst 1 ,
• J. M. Lavallee   ORCID: orcid.org/0000-0002-3028-7087 1 ,
• R. N. Lubowski 1   nAff17 ,
• C. Melikov 1   nAff18 ,
• L. A. Moore   ORCID: orcid.org/0000-0003-0239-6080 1   nAff19 ,
• E. E. Oldfield   ORCID: orcid.org/0000-0002-6181-1267 1 ,
• J. Paltseva 1   nAff20 ,
• A. M. Raffeld   ORCID: orcid.org/0000-0002-5036-6460 1 ,
• N. A. Randazzo 1   nAff21   nAff22 ,
• C. Schneider 1 ,
• N. Uludere Aragon 1   nAff23 &
• S. P. Hamburg 1  

Nature Climate Change ( 2024 ) Cite this article

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• Climate-change ecology
• Climate-change mitigation
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Viable nature-based climate solutions (NbCS) are needed to achieve climate goals expressed in international agreements like the Paris Accord. Many NbCS pathways have strong scientific foundations and can deliver meaningful climate benefits but effective mitigation is undermined by pathways with less scientific certainty. Here we couple an extensive literature review with an expert elicitation on 43 pathways and find that at present the most used pathways, such as tropical forest conservation, have a solid scientific basis for mitigation. However, the experts suggested that some pathways, many with carbon credit eligibility and market activity, remain uncertain in terms of their climate mitigation efficacy. Sources of uncertainty include incomplete GHG measurement and accounting. We recommend focusing on resolving those uncertainties before broadly scaling implementation of those pathways in quantitative emission or sequestration mitigation plans. If appropriate, those pathways should be supported for their cobenefits, such as biodiversity and food security.

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Nature-based climate solutions (NbCS) are conservation, restoration and improved management strategies (pathways) in natural and working ecosystems with the primary motivation to mitigate GHG emissions and remove CO 2 from the atmosphere 1 (similar to ecosystem-based mitigation 2 ). GHG mitigation through ecosystem stewardship is integral to meeting global climate goals, with the greatest benefit coming from near-term maximization of emission reductions, followed by CO 2 removal 3 . Many countries (for example, Indonesia, China and Colombia) use NbCS to demonstrate progress toward national climate commitments.

The scope of NbCS is narrower than that of nature-based solutions (NbS) which include interventions that prioritize non-climate benefits alongside climate (for example, biodiversity, food provisioning and water quality improvement) 4 . In many cases, GHG mitigation is considered a cobenefit that results from NbS actions focused on these other challenges 2 . In contrast, NbCS are broader than natural climate solutions, which are primarily focused on climate mitigation through conservation, restoration and improved land management, generally not moving ecosystems beyond their unmodified structure, function or composition 5 . NbCS may involve moving systems beyond their original function, for example by cultivating macroalgae in water deeper than their natural habitat.

The promise of NbCS has generated a proliferation of interest in using them in GHG mitigation plans 6 , 7 ; 104 of the 168 signatories to the Paris Accord included nature-based actions as part of their mitigation plans 8 . Success in long-term GHG management requires an accurate accounting of inputs and outputs to the atmosphere at scale, so NbCS credits must have robust, comprehensive and transparent scientific underpinnings 9 . Given the urgency of the climate problem, our goal is to identify NbCS pathways with a sufficient scientific foundation to provide broad confidence in their potential GHG mitigation impact, provide resources for confident implementation and identify priority research areas in more uncertain pathways. Evaluating implementation of mitigation projects is beyond our scope; this effort focuses on understanding the underlying science. The purpose is not evaluating any specific carbon crediting protocol or implementation framework but rather the current state of scientific understanding necessary to provide confidence in any NbCS.

In service of this goal, we first investigated nine biomes (boreal forests, coastal marine (salt marsh, mangrove, seagrass and coral reef), freshwater wetlands, grasslands, open ocean (large marine animal and mesopelagic zone biomass, seabed), peatlands, shrublands, temperate forests and tropical forests) and three cultivation types (agroforestry, croplands and macroalgae aquaculture); these were chosen because of their identified potential scale of global impact. In this context, impact is assessed as net GHG mitigation: the CO 2 sequestered or emissions reduced, for example, discounted by understood simultaneous emissions of other GHG (as when N 2 O is released simultaneously with carbon sequestration in cropland soils). From there, we identified 43 NbCS pathways which have been formally implemented (with or without market action) or informally proposed. We estimated the scale of mitigation impact for each pathway on the basis of this literature and, as a proxy measure of NbCS implementation, determined eligibility and activity under existing carbon crediting protocols. Eligibility means that the pathway is addressed by an existing GHG mitigation protocol; market activity means that credits are actively being bought under those eligibility requirements. We considered pathways across a spectrum from protection to improved management to restoration to manipulated systems, but some boundaries were necessary. We excluded primarily abiotically driven pathways (for example, ocean alkalinity enhancement) or where major land use or land-use trade-offs exist (for example, afforestation) 10 , 11 , 12 . Of the 43 pathways, 79% are at present eligible for carbon crediting (sometimes under several methodologies) and at least 65% of those have been implemented (Supplementary Table 1 ). This review was then appraised by 30 independent scholars (at least three per pathway; a complete review synthesis is given in the Supplementary Data ).

Consolidation of a broad body of scientific knowledge, with inherent variance, requires expert judgement. We used an expert elicitation process 13 , 14 , 15 with ten experts to place each proposed NbCS pathway into one of three readiness categories following their own assessment of the scientific literature, categorized by general sources of potential uncertainty: category 1, sufficient scientific basis to support a high-quality carbon accounting system or to support the development of such a system today; category 2, a >25% chance that focused research and reasonable funding would support development of high-quality carbon accounting (that is, move to category 1) within 5 years; or category 3, a <25% chance of development of high-quality carbon accounting within 5 years (for example, due to measurement challenges, unconstrained leakage, external factors which constrain viability).

If an expert ranked a pathway as category 2, they were also asked to rank general research needs to resolve: leakage/displacement (spillover to other areas), measuring, reporting and verification (the ability to quantify all salient stocks and fluxes), basic mechanisms of action (fundamental science), durability (ability to predict or compensate for uncertainty in timescale of effectiveness due to disturbances, climate change, human activity or other factors), geographic uncertainty (place-to-place variation), scaling potential (ability to estimate impact) and setting of a baseline (ability to estimate additionality over non-action; a counterfactual). To avoid biasing towards a particular a priori framework for evaluation of the scientific literature, reviewers could use their own framework for evaluating the NbCS literature about potential climate impact and so could choose to ignore or add relevant categorizations as well. Any pathway in category 1 would not need fundamental research for implementation; research gaps were considered too extensive for useful guidance on reducing uncertainty in category 3 pathways. Estimates of the global scale of likely potential impact (PgCO 2 e yr −1 ) and cobenefits were also collected from expert elicitors. See Methods and Supplementary Information for the survey instrument.

Four pathways with the highest current carbon market activity and high mitigation potential (tropical and temperate forest conservation and reforestation; Table 1 and Supplementary Data ), were consistently rated as high-confidence pathways in the expert elicitation survey. Other NbCS pathways, especially in the forestry sector, were rated relatively strongly by the experts for both confidence in scientific basis and scale of potential impact, with some spread across the experts (upper right quadrant, Fig. 1 ). Conversely, 13 pathways were consistently marked by experts as currently highly uncertain/low confidence (median score across experts: 2.5–3.0) and placed in category 3 (for example, cropland microbial amendments and coral reef restoration; Supplementary Tables 1 and 2 ). For the full review, including crediting protocols currently used, literature estimates of scale and details of sub-pathways, see Supplementary Data .

Pathways in the upper right quadrant have both high confidence in the scientific foundations and the largest potential scale of global impact; pathways in the lower left have the lowest confidence in our present scientific body of knowledge and an estimated smaller potential scale of impact. Designations of carbon credit eligibility under existing protocols and market activity at the present time are noted. Grassland enhanced mineral weathering (EMW) is not shown (mean category rating 2.9) as no scale of impact was estimated. See Supplementary Table 1 for specific pathway data. Bars represent 20th to 80th percentiles of individual estimates, if there was variability in estimates. A small amount of random noise was added to avoid overlap.

The experts assessed 26 pathways as having average confidence scores between 1.5 and 2.4, suggesting the potential for near-term resolution of uncertainties. This categorization arose from either consensus amongst experts on the uncertain potential (for example, boreal forest reforestation consistently rated category 2, with primary concerns about durability) or because experts disagreed, with some ranking category 1 and others category 3 (for example, pasture management). We note that where expert disagreement exists (seen as the spread of responses in Fig. 1 and Supplementary Table 1 ; also see Data availability for link to original data), this suggests caution against overconfidence in statements about these pathways. These results also suggest that confidence may be increased by targeted research on the identified sources of uncertainty (Supplementary Table 3 ).

Sources of uncertainty

Durability and baseline-setting were rated as high sources of uncertainty across all pathways ranked as category 2 by the experts (mean ratings of 3.6 and 3.4 out of 5, respectively; Supplementary Table 3 ). Understanding of mechanisms and geographic spread had the lowest uncertainty ratings (2.1 and 2.3, respectively), showing confidence in the basic science. Different subsets of pathways had different prioritizations, however, suggesting different research needs: forest-centric pathways were most uncertain in their durability and additionality (3.8 and 3.4, respectively), suggesting concerns about long-term climate and disturbance trajectories. Agricultural and grassland systems, however, had higher uncertainty in measurement methods and additionality (3.9 and 3.5 respectively). Although there were concerns about durability from some experts (for example, due to sea-level rise), some coastal blue carbon pathways such as mangrove restoration (mean category ranking: 1.7 (20th to 80th percentile 1.0–2.0)) have higher confidence than others (for example, seagrass restoration: mean category ranking 2.8, 20th to 80th percentile 2.6–3.0)), which are relatively poorly constrained in terms of net radiative forcing potential despite a potentially large carbon impact (seagrass median: 1.60 PgCO 2 e yr −1 ; see Supplementary Data for more scientific literature estimates).

Scale of impact

For those pathways with lower categorization by the expert elicitation (category 2 or 3) at the present time, scale of global impact is a potential heuristic for prioritizing further research. High variability, often two orders of magnitude, was evident in the mean estimated potential PgCO 2 e yr −1 impacts for the different pathways (Fig. 1 and Supplementary Table 2 ) and the review of the literature found even larger ranges produced by individual studies (Supplementary Data ). A probable cause of this wide range was different constraints on the estimated potential, with some studies focusing on potential maximum impact and others on more constrained realizable impacts. Only avoided loss of tropical forest and cropland biochar amendment were consistently estimated as having the likely potential to mitigate >2 PgCO 2 e yr −1 , although biochar was considered more uncertain by experts due to other factors germane to its overall viability as a climate solution, averaging a categorization of 2.2. The next four highest potential impact pathways, ranging from 1.6 to 1.7 PgCO 2 e yr −1 , spanned the spectrum from high readiness (temperate forest restoration) to moderate (cropland conversion from annual to perennial vegetation and grassland restoration) to low (seagrass restoration, with main uncertainties around scale of potential impact and durability).

There was high variability in the elicitors’ estimated potential scale of impact, even in pathways with strong support, such as tropical forest avoided loss (20th to 80th percentile confidence interval: 1–8 PgCO 2 e yr −1 ), again emphasizing the importance of consistent definitions and constraints on how NbCS are measured, evaluated and then used in broad-scale climate change mitigation planning and budgeting. Generally, as pathway readiness decreased (moving from category 1 to 3), the elicitor-estimated estimates of GHG mitigation potential decreased (Supplementary Fig. 1 ). Note that individual studies from the scientific literature may have higher or lower estimates (Supplementary Data ).

Expert elicitation meta-analyses suggest that 6–12 responses are sufficient for a robust and stable quantification of responses 15 . We tested that assumption via a Monte Carlo-based sensitivity assessment. Readiness categorizations by the ten experts were robust to a Monte Carlo simulation test, where further samples were randomly drawn from the observed distribution of responses: mean difference between the original and the boot-strapped data was 0.02 (s.d. = 0.05) with an absolute difference average of 0.06 (s.d. = 0.06). The maximum difference in readiness categorization means across all pathways was 0.20 (s.d. = 0.20) (Supplementary Table 2 ). The full dataset of responses is available online (see ʻData availabilityʼ).

These results highlight opportunities to accelerate implementation of NbCS in well-supported pathways and identify critical research needs in others (Fig. 1 ). We suggest focusing future efforts on resolving identified uncertainties for pathways at the intersection between moderate average readiness (for example, mean categorizations between ~1.5 and 2.0) and high potential impact (for example, median >0.5 PgCO 2 e yr −1 ; Supplementary Table 1 ): agroforestry, improved tropical and temperate forest management, tropical and boreal peatlands avoided loss and peatland restoration. Many, although not all, experts identified durability and baseline/additionality as key concerns to resolve in those systems; research explicitly targeted at those specific uncertainties (Supplementary Table 3 ) could rapidly improve confidence in those pathways.

We recommend a secondary research focus on the lower ranked (mean category 2.0 to 3.0) pathways with estimated potential impacts >1 PgCO 2 e yr −1 (Supplementary Fig. 2 ). For these pathways, explicit, quantitative incorporation into broad-scale GHG management plans will require further focus on systems-level carbon/GHG understandings to inspire confidence at all stages of action and/or identifying locations likely to support durable GHG mitigation, for example ref. 16 . Examples of this group include avoided loss and degradation of boreal forests (for example, fire, pests and pathogens and albedo 16 ) and effective mesopelagic fishery management, which some individual studies estimate would avoid future reductions of the currently sequestered 1.5–2.0 PgC yr −1 (refs. 17 , 18 ). These pathways may turn out to have higher or lower potential than the expert review suggests, on the basis of individual studies (Supplementary Data ) but strong support will require further, independent verification of that potential.

We note that category 3 rankings by expert elicitation do not necessarily imply non-viability but simply that much more research is needed to confidently incorporate actions into quantitative GHG mitigation plans. We found an unsurprising trend of lower readiness categorization with lower pathway familiarity (Supplementary Fig. 3 ). This correlation may result from two, non-exclusive potential causes: (1) lower elicitor expertise in some pathways (inevitable, although the panel was explicitly chosen for global perspectives, connections and diverse specialties) and (2) an actual lack of scientific evidence in the literature, which leads to that self-reported lack of familiarity, a common finding in the literature review (Supplementary Data ). Both explanations suggest a need to better consolidate, develop and disseminate the science in each pathway for global utility and recognition.

Our focus on GHG-related benefits in no way diminishes the substantial conservation, environmental and social cobenefits of these pathways (Supplementary Table 4 ), which often exceed their perceived climate benefits 1 , 19 , 20 , 21 . Where experts found climate impacts to remain highly uncertain but other NbS benefits are clear (for example, biodiversity and water quality; Supplementary Table 4 ), other incentives or financing mechanisms independent of carbon crediting should be pursued. While the goals here directly relate to using NbCS as a reliably quantifiable part of global climate action planning and thus strong GHG-related scientific foundations, non-climate NbS projects may provide climate benefits that are less well constrained (and thus less useful from a GHG budgeting standpoint) but also valuable. Potential trade-offs, if any, between ecosystem services and management actions, such as biodiversity and positive GHG outcomes, should be explored to ensure the best realization of desired goals 2 .

Finally, our focus in this study was on broad-scale NbCS potential in quantitative mitigation planning because of the principal and necessary role of NbCS in overall global warming targets. We recognize the range of project conditions that may increase, or decrease, the rigour of any pathway outside the global-scale focus here. We did not specifically evaluate the large and increasing number of crediting concepts (by pathway: Supplementary Data ), focusing rather on the underlying scientific body of knowledge within those pathways. Some broad pathways may have better defined sub-pathways within them, with a smaller potential scale of impact but potentially lower uncertainty (for example, macroalgae harvest cycling). Poorly enacted NbCS actions and/or crediting methodologies at project scales may result in loss of benefits even from high-ranking pathways 22 , 23 , 24 and attention to implementation should be paramount. Conversely, strong, careful project-scale methodologies may make lower readiness pathways beneficial for a given site.

Viable NbCS are vital to global climate change mitigation but NbCS pathways that lack strong scientific underpinnings threaten global accounting by potentially overestimating future climate benefits and eroding public trust in rigorous natural solutions. Both the review of the scientific literature and the expert elicitation survey identified high potential ready-to-implement pathways (for example, tropical reforestation), reinforcing present use of NbCS in planning.

However, uncertainty remains about the quantifiable GHG mitigation of some active and nascent NbCS pathways. On the basis of the expert elicitation survey and review of the scientific literature, we are concerned that large-scale implementation of less scientifically well-founded NbCS pathways in mitigation plans may undermine net GHG budget planning; those pathways require more study before they can be confidently promoted at broad scales and life-cycle analyses to integrate system-level emissions when calculating totals. The expert elicitation judgements suggest a precautionary approach to scaling lower confidence pathways until the scientific foundations are strengthened, especially for NbCS pathways with insufficient measurement and monitoring 10 , 24 , 25 or poorly understood or measured net GHG mitigation potentials 16 , 26 , 27 , 28 . While the need to implement more NbCS pathways for reducing GHG emissions and removing carbon from the atmosphere is urgent, advancing the implementation of poorly quantified pathways (in relation to their GHG mitigation efficacy) could give the false impression that they can balance ongoing, fossil emissions, thereby undermining overall support for more viable NbCS pathways. Explicitly targeting research to resolve these uncertainties in the baseline science could greatly bolster confidence in the less-established NbCS pathways, benefiting efforts to reduce GHG concentrations 29 .

The results of this study should inform both market-based mechanisms and non-market approaches to NbCS pathway management. Research and action that elucidates and advances pathways to ensure a solid scientific basis will provide confidence in the foundation for successfully implementing NbCS as a core component of global GHG management.

NbCS pathway selection

We synthesized scientific publications for nine biomes (boreal forests, coastal blue carbon, freshwater wetlands, grasslands, open ocean blue carbon, peatlands, shrublands, temperate forests and tropical forests) and three cultivation types (agroforestry, croplands and macroalgae aquaculture) (hereafter, systems) and the different pathways through which they may be able to remove carbon or reduce GHG emissions. Shrublands and grasslands were considered as independent ecosystems; nonetheless, we acknowledge that there is overlap in the numbers presented here because shrublands are often included with grasslands 5 , 30 , 31 , 32 , 33 .

The 12 systems were chosen because they have each been identified as having potential for emissions reductions or carbon removal at globally relevant scales. Within these systems, we identified 43 pathways which either have carbon credit protocols formally established or informally proposed for review (non-carbon associated credits were not evaluated). We obtained data on carbon crediting protocols from international, national and regional organizations and registries, such as Verra, American Carbon Registry, Climate Action Reserve, Gold Standard, Clean Development Mechanism, FAO and Nori. We also obtained data from the Voluntary Registry Offsets Database developed by the Berkeley Carbon Trading Project and Carbon Direct company 34 . While we found evidence of more Chinese carbon crediting protocols, we were not able to review these because of limited publicly available information. To maintain clarity and avoid misrepresentation, we used the language as written in each protocol. A full list of the organizations and registries for each system can be found in the Supplementary Data .

Literature searches and synthesis

We reviewed scientific literature and reviews (for example, IPCC special reports) to identify studies reporting data on carbon stocks, GHG dynamics and sequestration potential of each system. Peer-reviewed studies and meta-analyses were identified on Scopus, Web of Science and Google Scholar using simple queries combining the specific practice or pathway names or synonyms (for example, no-tillage, soil amendments, reduced stocking rates, improved forest management, avoided forest conversion and degradation, avoided mangrove conversion and degradation) and the following search terms: ‘carbon storage’, ‘carbon stocks’, ‘carbon sequestration’, ‘carbon sequestration potential’, ‘additional carbon storage’, ‘carbon dynamics’, ‘areal extent’ or ‘global’.

The full literature review was conducted between January and October 2021. We solicited an independent, external review of the syntheses (obtaining from at least three external reviewers per natural or working system; see p. 2 of the Supplementary Data ) as a second check against missing key papers or misinterpretation of data. The review was generally completed in March 2022. Data from additional relevant citations were added through October 2022 as they were discovered. For a complete list of all literature cited, see pp. 217–249 of the Supplementary Data .

From candidate papers, the papers were considered if their results/data could be applied to the following central questions:

How much carbon is stored (globally) at present in the system (total and on average per hectare) and what is the confidence?

At the global level, is the system a carbon source or sink at this time? What is the business-as-usual projection for its carbon dynamics?

Is it possible, through active management, to either increase net carbon sequestration in the system or prevent carbon emissions from that system? (Note that other GHG emissions and forcings were included here as well.)

What is the range of estimates for how much extra carbon could be sequestered globally?

How much confidence do we have in the present methods to detect any net increases in carbon sequestration in a system or net changes in areal extent of that?

From each paper, quantitative estimates for the above questions were extracted for each pathway, including any descriptive information/metadata necessary to understand the estimate. In addition, information on sample size, sampling scheme, geographic coverage, timeline of study, timeline of projections (if applicable) and specific study contexts (for example, wind-break agroforestry) were recorded.

We also tracked where the literature identified trade-offs between carbon sequestered or CO 2 emissions reduced and emissions of other GHG (for example, N 2 O or methane) for questions three and five above. For example, wetland restoration can result in increased CO 2 uptake from the atmosphere. However, it can also increase methane and N 2 O emissions to the atmosphere. Experts were asked to consider the uncertainty in assessing net GHG mitigation as they categorized the NbCS pathways.

Inclusion of each pathway in mitigation protocols and the specific carbon registries involved were also identified. These results are reported (grouped or individually as appropriate) in the Supplementary Data , organized by the central questions and including textual information for interpretation. The data and protocol summaries for each of the 12 systems were reviewed by at least three scientists each and accordingly revised.

These summaries were provided to the expert elicitation group as optional background information.

Unit conversions

Since this synthesis draws on literature from several sources that use different methods and units, all carbon measurements were standardized to the International System of Units (SI units). When referring to total stocks for each system, numbers are reported in SI units of elemental carbon (that is, PgC). When referring to mitigation potential, elemental carbon was converted to CO 2 by multiplying by 3.67. Differences in methodology, such as soil sampling depth, make it difficult to standardize across studies. Where applicable, the specific measurement used to develop each stock estimate is reported.

Expert elicitation process

To assess conclusions brought about by the initial review process described above, we conducted an expert elicitation survey to consolidate and add further, independent assessments to the original literature review. The expert elicitation survey design followed best practice recommendations 14 , with a focus on participant selection, explicitly defining uncertainty, minimizing cognitive and overconfidence biases and clarity of focus. Research on expert elicitation suggests that 6–12 responses are sufficient for a stable quantification of responses 15 . We identified >40 potential experts via a broad survey of leading academics, science-oriented NGO and government agency publications and products. These individuals have published on several NbCS pathways or could represent larger research efforts that spanned the NbCS under consideration. Careful attention was paid to the gender and sectoral breakdown of respondents to ensure equitable representation. Of the invitees, ten completed the full elicitation effort. Experts were offered compensation for their time.

Implementation of the expert elicitation process followed the IDEA protocol 15 . Briefly, after a short introductory interview, the survey was sent to the participants. Results were anonymized and standardized (methods below) and a meeting held with the entire group to discuss the initial results and calibrate understanding of questions. The purpose of this meeting was not to develop consensus on a singular answer but to discuss and ensure that all questions are being considered in the same way (for example, clarifying any potentially confusing language, discussing any questions that emerged as part of the process). The experts then revisited their initial rankings to provide final, anonymous rankings which were compiled in the same way. These final rankings are the results presented here and may be the same or different from the initial rankings, which were discarded.

Survey questions

The expert elicitation survey comprised five questions for each pathway. The data were collected via Google Forms and collated anonymously at the level of pathways, with each respondent contributing one datapoint for each pathway. The experts reported their familiarity (or the familiarity of the organization whose work they were representing) with the pathway and other cobenefits for the pathways.

The initial question ranked the NbCS pathway by category, from one to three.

Category 1 was defined as a pathway with sufficient scientific knowledge to support a high-quality carbon accounting system today (for example, meets the scientific criteria identified in the WWF-EDF-Oeko Institut and ICAO TAB) or to support the development of such a system today. The intended interpretation is that sufficient science is available for quantifying and verifying net GHG mitigation. Note that experts were not required to reference any given ‘high-quality’ crediting framework, which were provided only as examples. In other words, the evaluation was not intended to rank a given framework (for example, ref. 35 ) but rather expert confidence in the fundamental scientific understandings that underpin potential for carbon accounting overall. To this end, no categorization of uncertainty was required (reviewers could skip categorizations they felt were not necessary) and space was available to fill in new categories by individual reviewers (if they felt a category was missing or needed). Uncertainties at this category 1 level are deemed ‘acceptable’, for example, not precluding accounting now, although more research may further substantiate high-quality credits.

Category 2 pathways have a good chance (>25%) that with more research and within the next 5 years, the pathway could be developed into a high-quality pathway for carbon accounting and as a nature-based climate solution pathway. For these pathways, further understanding is needed for factors such as baseline processes, long-term stability, unconstrained fluxes, possible leakage or other before labelling as category 1 but the expert is confident that information can be developed, in 5 years or less, with more work. The >25% chance threshold and 5-year timeframe were determined a priori to reflect and identify pathways that experts identified as having the potential to meet the Paris Accord 2030 goal. Other thresholds (for example, longer timeframes) could have been chosen, which would impact the relative distribution of pathways in categories 2 and 3 (for example, a longer timeframe allowed could move some pathways from category 3 into category 2, for some reviewers). We emphasize that category 3 pathways do not necessarily mean non-valuable approaches but longer timeframes required for research than the one set here.

Category 3 responses denoted pathways that the expert thought had little chance (<25%) that with more research and within the next 5 years, this pathway could be developed into a suitable pathway for managing as a natural solutions pathway, either because present evidence already suggests GHG reduction is not likely to be viable, co-emissions or other biophysical feedbacks may offset those gains or because understanding of key factors is lacking and unlikely to be developed within the next 5 years. Notably, the last does not mean that the NbCS pathway is not valid or viable in the long-term, simply that physical and biological understandings are probably not established enough to enable scientific rigorous and valid NbCS activity in the near term.

The second question asked the experts to identify research gaps associated with those that they ranked as category 2 pathways to determine focal areas for further research. The experts were asked to rank concerns about durability (ability to predict or compensate for uncertainty in timescale of effectiveness due to disturbances, climate change, human activity or other factors), geographic uncertainty (place-to-place variation), leakage or displacement (spillover of activities to other areas), measuring, reporting and verification (MRV, referring to the ability to quantify all salient stocks and fluxes to fully assess climate impacts), basic mechanisms of action (fundamental science), scaling potential (ability to estimate potential growth) and setting of a baseline (ability to reasonably quantify additionality over non-action, a counterfactual). Respondents could also enter a different category if desired. For complete definitions of these categories, see the survey instrument ( Supplementary Information ). This question was not asked if the expert ranked the pathway as category 1, as those were deemed acceptable, or for category 3, respecting the substantial uncertainty in that rating. Note that responses were individual and so the same NbCS pathway could receive (for example) several individual category 1 rankings, which would indicate reasonable confidence from those experts, and several category 2 rankings from others, which would indicate that those reviewers have lingering concerns about the scientific basis, along with their rankings of the remaining key uncertainties in those pathways. These are important considerations, as they reflect the diversity of opinions and research priorities; individual responses are publicly available (anonymized: https://doi.org/10.5281/zenodo.7859146 ).

The third question involved quantification of the potential for moving from category 2 to 1 explicitly. Following ref. 14 , the respondents first reported the lowest plausible value for the potential likelihood of movement (representing the lower end of a 95% confidence interval), then the upper likelihood and then their best guess for the median/most likely probability. They were also asked for the odds that their chosen interval contained the true value, which was used to scale responses to standard 80% credible intervals and limit overconfidence bias 13 , 15 . This question was not asked if the expert ranked the pathway as category 3, respecting the substantial uncertainty in that rating.

The fourth question involved the scale of potential impact from the NbCS, given the range of uncertainties associated with effectiveness, area of applicability and other factors. The question followed the same pattern as the third, first asking about lowest, then highest, then best estimate for potential scale of impact (in PgCO 2 e yr −1 ). Experts were again asked to express their confidence in their own range, which was used to scale to a standard 80% credible interval. This estimate represents a consolidation of the best-available science by the reviewers. For a complete review including individual studies and their respective findings, see the Supplementary Data . This question was not asked if the expert ranked the pathway as category 3, respecting the substantial uncertainty in that rating.

Final results

After collection of the final survey responses, results were anonymized and compiled by pathway. For overall visualization and discussion purposes, responses were combined into a mean and 20th to 80th percentile range. The strength of the expert elicitation process lies in the collection of several independent assessments. Those different responses represent real differences in data interpretation and synthesis ascribed by experts. This can have meaningful impacts on decision-making by different individuals and organizations (for example, those that are more optimistic or pessimistic about any given pathway). Therefore, individual anonymous responses were retained by pathway to show the diversity of responses for any given pathway. The experts surveyed, despite their broad range of expertise, ranked themselves as less familiar with category 3 pathways than category 1 or 2 (linear regression, P  < 0.001, F  = 59.6 2, 394 ); this could be because of a lack of appropriate experts—although they represented all principal fields—or simply because the data are limited in those areas.

Sensitivity

To check for robustness against sample size variation, we conducted a Monte Carlo sensitivity analysis of the data on each pathway to generate responses of a further ten hypothetical experts. Briefly, the extra samples were randomly drawn from the observed category ranking mean and standard deviations for each individual pathway and appended to the original list; values <1 or >3 were truncated to those values. This analysis resulted in only minor differences in the mean categorization across all pathways: the mean difference between the original and the boot-strapped data was 0.02 (s.d. = 0.05) with an absolute difference average of 0.06 (s.d. = 0.06). The maximum difference in means across all pathways was 0.20 (s.d. = 0.20) (Supplementary Table 2 ). The results suggest that the response values are stable to additional responses.

All processing was done in R 36 , with packages including fmsb 37 and forcats 38 .

Data availability

Anonymized expert elicitation responses are available on Zenodo 39 : https://doi.org/10.5281/zenodo.7859146 .

Code availability

R code for analysis available on Zenodo 39 : https://doi.org/10.5281/zenodo.7859146 .

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Acknowledgements

This research was supported through gifts to the Environmental Defense Fund from the Bezos Earth Fund, King Philanthropies and Arcadia, a charitable fund of L. Rausing and P. Baldwin. We thank J. Rudek for help assembling the review and 30 experts who reviewed some or all of those data and protocol summaries (Supplementary Data ). S.M. was supported by a cooperative agreement between the National Science Foundation and Battelle that sponsors the National Ecological Observatory Network programme.

Author information

Present address: Department of Atmospheric and Oceanic Sciences, University of Wisconsin-Madison, Madison, WI, USA

Present address: AtmoFacts, Longmont, CO, USA

R. N. Lubowski

Present address: Lombard Odier Investment Managers, New York, NY, USA

Present address: Ecological Carbon Offset Partners LLC, dba EP Carbon, Minneapolis, MN, USA

L. A. Moore

Present address: , San Francisco, CA, USA

J. Paltseva

Present address: ART, Arlington, VA, USA

N. A. Randazzo

Present address: NASA/GSFC, Greenbelt, MD, USA

Present address: University of Maryland, College Park, MD, USA

N. Uludere Aragon

Present address: Numerical Terradynamic Simulation Group, University of Montana, Missoula, MT, USA

These authors contributed equally: B. Buma, D. R. Gordon.

Authors and Affiliations

Environmental Defense Fund, New York, NY, USA

B. Buma, D. R. Gordon, K. M. Kleisner, A. Bartuska, J. R. Collins, A. J. Eagle, R. Fujita, E. Holst, J. M. Lavallee, R. N. Lubowski, C. Melikov, L. A. Moore, E. E. Oldfield, J. Paltseva, A. M. Raffeld, N. A. Randazzo, C. Schneider, N. Uludere Aragon & S. P. Hamburg

Department of Integrative Biology, University of Colorado, Denver, CO, USA

Department of Biology, University of Florida, Gainesville, FL, USA

D. R. Gordon

Resources for the Future, Washington, DC, USA

A. Bartuska

International Arctic Research Center, University of Alaska, Fairbanks, AK, USA

Department of Ecology Evolution and Environmental Biology and the Climate School, Columbia University, New York, NY, USA

The Nature Conservancy, Arlington, VA, USA

Faculty of Environment, Science and Economy, University of Exeter, Exeter, UK

P. Friedlingstein

Laboratoire de Météorologie Dynamique/Institut Pierre-Simon Laplace, CNRS, Ecole Normale Supérieure/Université PSL, Sorbonne Université, Ecole Polytechnique, Palaiseau, France

National Ecological Observatory Network, Battelle, Boulder, CO, USA

Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA

O’Neill School of Public and Environmental Affairs, Indiana University, Bloomington, IN, USA

Department of Environmental Science and Policy, University of California, Davis, CA, USA

J. N. Sanchirico

Department of Marine Chemistry & Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, MA, USA

J. R. Collins

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D.R.G. and B.B. conceived of and executed the study design. D.R.G., K.M.K., J.R.C., A.J.E., R.F., E.H., J.M.L., R.N.L., C.M., L.A.M., E.E.O., J.P., A.M.R., N.A.R., C.S. and N.U.A. coordinated and conducted the literature review. G.M. and B.B. primarily designed the survey. A. Bartuska, A. Bidlack, B.B., J.N.S., K.N., P.E., P.F., R.D. and S.M. contributed to the elicitation. B.B. conducted the analysis and coding. S.P.H. coordinated funding. B.B. and D.R.G. were primary writers; all authors were invited to contribute to the initial drafting.

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Correspondence to B. Buma .

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Competing interests.

The authors declare no competing interests. In the interest of full transparency, we note that while B.B., D.R.G., K.M.K., A.B., J.R.C., A.J.E., R.F., E.H., J.M.L., R.N.L., C.M., L.A.M., E.E.O., J.P., A.M.R., N.A.R., C.S., N.U.A., S.P.H. and P.E. are employed by organizations that have taken positions on specific NbCS frameworks or carbon crediting pathways (not the focus of this work), none have financial or other competing interest in any of the pathways and all relied on independent science in their contributions to the work.

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Nature Climate Change thanks Camila Donatti, Connor Nolan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary information.

Supplementary Tables 1–4, Figs. 1–3 and survey instrument.

Supplementary Data

Literature review and list of reviewers.

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Buma, B., Gordon, D.R., Kleisner, K.M. et al. Expert review of the science underlying nature-based climate solutions. Nat. Clim. Chang. (2024). https://doi.org/10.1038/s41558-024-01960-0

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Received : 24 April 2023

Accepted : 20 February 2024

Published : 21 March 2024

DOI : https://doi.org/10.1038/s41558-024-01960-0

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