Developing countries can adapt to climate change effectively using nature-based solutions

Climate change disproportionately affects low- and middle-income countries (LMICs), particularly in agriculture and coastal regions. Sustainable Development Goals (SDGs) emphasize the need for interventions that address climate change adaptation while promoting poverty reduction, economic stability, and public health. However, a fragmented understanding of the effectiveness of various interventions hinders progress. This study addresses this gap by systematically reviewing quantitative evidence on the effectiveness of climate change adaptation interventions in LMICs, focusing on their impact on risk reduction and development-related outcomes. The research question guiding this study is: To what extent are different types of climate change adaptation interventions in the agricultural and coastal sectors effective in achieving risk-reduction and development outcomes in LMICs? This research contributes to two key debates: the relative effectiveness of interventions targeting climate protection versus sustainable development, and the comparison of hard (technological and infrastructure-based) versus soft (behavioral or institutional) interventions, including the growing interest in Nature-based Solutions (NbS).

Existing literature provides growing evidence on the effectiveness of climate change adaptation interventions in LMICs, particularly in agriculture and coastal areas. Studies in the agricultural sector focus on interventions targeting farmer behavior, agricultural productivity, and livelihood resilience. Coastal sector studies examine interventions such as NbS to prevent economic damage and reduce livelihood vulnerability. However, an integrative synthesis of adaptation effectiveness is lacking, with information scattered across studies and outcome types. Previous syntheses have focused on specific aspects such as state-of-the-art, metrics, planning, financing, or specific intervention types, often neglecting the distinction between LMICs and industrialized countries or sector-specific differences.

This study employed a systematic review methodology. The data was drawn from an existing evidence gap map (EGM) on adaptation in LMICs, containing quantitative studies accessible online. The EGM included quantitative studies using correlation, impact, or review methods published between 2007 and 2018, primarily in English. The studies were categorized by sector (agricultural and coastal), intervention type (NbS, built infrastructure, technological, informational, institutional, financial, social/behavioral), and outcome category (reduction of climate hazard and exposure, reduction of social or economic vulnerability, contribution to the enabling environment). A coding matrix was developed to capture relevant information from each study. Three coders independently coded the data, utilizing a rigorous qualitative consensus approach to ensure reliability. The coding process involved a collaborative initial phase followed by independent coding of separate study batches, with collaborative resolution of any discrepancies. Effect sizes were coded using an ordinal scale (small, medium, large), considering both quantitative measures and qualitative descriptions from the studies. The final dataset consisted of 363 empirical observations from 103 studies (84 agricultural, 19 coastal). Data limitations included potential sample bias due to language restrictions, underrepresentation of interventions better suited for qualitative evaluations, publication bias favoring positive results, challenges in identifying adaptation interventions in non-adaptation focused studies, and difficulties in linking studies to specific climate change threats. The study also notes the lack of sufficient covariate information to account for other factors influencing the effectiveness of interventions and the challenges in coding diverse studies.

The systematic review revealed that adaptation interventions can be effective in achieving both risk reduction and development-related outcomes. However, the effectiveness varies across sectors and intervention types. **Coastal Sector:** NbS were strongly associated with positive effects on risk reduction (29 positive vs. 9 neutral/negative observations), while social/behavioral interventions showed more neutral or negative effects on development-related outcomes (17 neutral/negative vs. 13 positive observations). Technological interventions also demonstrated mostly negative effects. Relocation of populations from coastal areas was linked to negative development outcomes in one specific study. **Agricultural Sector:** Most interventions (69% of observations) showed positive effects. Informational and technological interventions demonstrated the most positive effects. Financial and social/behavioral interventions showed a considerable number of negative effects. **Overall:** NbS showed consistently positive effects across all outcome categories in both sectors. Informational interventions in the agricultural sector had clearly positive effects on both risk reduction and development. Built infrastructure and NbS also showed positive effects on reducing social and economic vulnerability in agriculture. However, some interventions exhibited negative effects, particularly social/behavioral interventions on development outcomes in the coastal sector. Analysis of combined interventions showed that combinations had only slightly more positive effects than single interventions, with most combinations involving NbS, technological, and social/behavioral interventions. The study also analyzed mean effects for different combinations of intervention types and outcome categories, demonstrating varying degrees of positive and negative impacts across sectors. For example, the positive mean effects of institutional interventions and built infrastructure in the coastal sector were particularly high, although with low observation numbers.

The findings address the research question by demonstrating that adaptation interventions can be effective for both risk reduction and development, but effectiveness varies significantly based on sector and intervention type. The strong positive impact of NbS across sectors supports the argument for their multiple co-benefits. The positive results of informational interventions in agriculture highlight the role of knowledge and learning in building resilience. The study’s results show a clear opportunity for policymakers and practitioners to select interventions tailored to specific outcomes and contexts. The identification of negative effects of some interventions emphasizes the importance of careful monitoring and evaluation. Further research should investigate the synergies and tradeoffs between various intervention combinations to enhance adaptation effectiveness.

This study provides valuable insights into the effectiveness of climate change adaptation interventions in LMICs. NbS stand out as a highly effective approach for achieving both risk reduction and development goals in both coastal and agricultural sectors. The findings emphasize the need for tailored interventions considering specific outcomes and contexts, alongside careful monitoring to identify and mitigate potential negative effects. Future research should focus on exploring synergies between intervention combinations and improving the evidence base, particularly in the coastal sector and concerning long-term development outcomes. Integrating qualitative data to understand community perceptions will strengthen the understanding of effective interventions.

The study acknowledges several limitations. The keyword search focused on English language studies, potentially excluding relevant articles in other languages. The reliance on quantitative studies may have underrepresented interventions more suitable for qualitative evaluations. Publication bias may have overrepresented interventions with positive outcomes. The challenges in identifying adaptation interventions in non-adaptation focused studies and the absence of sufficient covariate data to account for other influential factors also affected the study. The heterogeneity of interventions and outcomes made a rigorous quantitative meta-analysis impossible.