Climate change increasingly threatens agricultural production, making it urgent to promote farmers’ adaptation to safeguard food security and livelihood stability. Drawing on the sustainable livelihoods framework (SLF), this study combines fuzzy-set qualitative comparative analysis (fsQCA) and propensity score matching (PSM) to examine the livelihood-capital configurations that drive farmers’ climate adaptation behavior and assess their loss-reduction effects. The results show that configurations involving high information, financial, natural, and human capital promote climate adaptation behavior, while high social capital exhibits a substitution effect. In contrast, configurations characterized by low social, human, and physical capital lead to low-level climate adaptation behavior, revealing causal asymmetry. Moreover, configurations leading to high-level climate adaptation behavior differ in their loss-reduction performance, with the combination of high information, financial, and physical capital producing the most robust effect. These findings provide configurational evidence for targeted climate adaptation policies in resource-constrained rural areas.
This review systematically examines negative emission technologies (NETs) as essential tools for carbon neutrality, covering major approaches, including bioenergy with carbon capture and storage (BECCS), direct air carbon capture and storage (DACCS), enhanced rock weathering (ERW), ocean-based methods, soil and forest carbon sequestration, and emerging hybrid systems, all categorized under the unified framework of nature-based solutions (NbS) versus technology-based solutions (TbS), a central distinction in climate governance. It assesses these technologies by technology readiness, demonstration status, scalability, and geographical suitability. Key findings underscore NETs’ critical role in offsetting residual hard-to-abate emissions and enabling net-negative emission scenarios. However, large-scale deployment faces challenges, including environmental impacts (land, water, biodiversity) and long-term sequestration safety, requiring rigorous monitoring. Future priorities include overcoming bottlenecks in materials, hydrogen storage, and electrochemical reduction; improving multi-scale modeling with life-cycle assessment and uncertainty quantification; and advancing cross-sector integration. Ultimately, success demands interdisciplinary collaboration, sustained innovation, and strategic portfolio optimisation to balance environmental, economic, and technical feasibility in the low-carbon transition. Notably, with particular focus on emerging hybrid systems, this review complements and extends earlier overviews of the NETs field.
Access to credit is essential for financing climate adaptation in agriculture, yet persistent credit market inefficiencies and inequities limit farmers’ adaptive capacity in developing countries. The combined effects of credit market imperfections, socio-economic characteristics, and climate vulnerability on adaptation investment remain insufficiently understood. This study addresses this gap using cross-sectional data from 498 rice farmers in the Pabna and Natore districts of northwest Bangladesh. Using a multi-stage econometric and behavioural framework—combining a 2SLS-Probit model, Heckman two-step selection, Theory of Planned Behaviour-based hierarchical regression, and a Multivariate Probit robustness check—the study examines credit market inefficiencies, adaptation investment decisions, farmers” intentions toward credit access, and the adoption of climate adaptation practices. The results show that high interest rates and stringent collateral requirements restrict access to formal credit, forcing many farmers to rely on informal and semi-formal sources, while improved formal credit access significantly increases climate adaptation investment, particularly among larger farms, with small and marginal farmers facing greater constraints. The findings underscore the need for expanded formal lending, flexible collateral arrangements, concessional climate finance, and credit programs integrated with climate-smart technologies and financial literacy to strengthen climate resilience and advance Bangladesh”s National Adaptation Plan, Delta Plan 2100, and SDGs 1, 2, and 13.
Climate change is intensifying rainfall erosivity and the frequency of extreme events across semi-arid regions of the Global South, amplifying the vulnerability of hillslopes and watersheds that sustain food and water security for more than 60 million people. Soil loss rates that already reach 36 Gt yr ^-1 globally compromise agricultural productivity and water quality, directly affecting the well-being of rural communities and their livelihood opportunities. Nature-based solutions (NbS) constitute adaptation strategies endorsed by the IUCN, yet the dispersion of results across functional macro-groups and fibrous materials hinders prioritization in management programs and regional cooperation initiatives. This study assesses the comparative effectiveness of five functional NbS macro-groups in reducing soil loss in field trials with natural materials, and tests the geographic transferability of the predictive model across a database spanning 18 countries to inform climate adaptation strategies in semi-arid regions. We conducted a PRISMA 2020 meta-analysis comprising 93 control–treatment comparisons from 43 studies, classified into five functional macro-groups and seven aggregated intervention types. The random-effects model indicated a mean reduction of 84.8
The refined quantification of carbon emissions and an improved understanding of their drivers are fundamental to designing differentiated carbon mitigation policies and enabling effective climate governance. Despite advances in emission estimation, sub-provincial data scarcity and limited insight into spatially heterogeneous drivers remain key challenges. Taking the Yangtze River Delta (YRD) region as an example, this study integrated provincial carbon emission data with nighttime light data to develop a city-level carbon emission inversion model. Subsequently, an interpretable machine learning technique was employed to identify the core drivers of carbon emissions. The results showed that: 1) at the provincial level, Jiangsu recorded the highest carbon emissions; at the city level, Shanghai, Suzhou, and Nanjing exhibited the highest carbon emissions, whereas the lowest carbon emissions were observed in mountainous and ecologically protected zones in southern Anhui and western Zhejiang. 2) global spatial autocorrelation analysis revealed significant positive spatial autocorrelation in YRD carbon emissions, while local spatial autocorrelation analysis further identified stable high–high clusters centered along the Shanghai–Suzhou corridor. 3) The optimal XGBoost model, combined with the SHapley Additive exPlanations framework and Partial Dependence Plots, identified four dominant drivers of city-level carbon emissions: population size, technological innovation, openness level, and per capita GDP, and further revealed significant non-linear and interaction effects among them. Notably, cities across different carbon emission zones exhibited heterogeneous characteristics. This study breaks through the “homogeneous impact” assumption of traditional linear models, providing a theoretical basis and practical pathways for differentiated “zone-specific” governance.
The issue of global warming is becoming increasingly severe, and achieving China’s “Dual Carbon” goals relies on the precise management and control of industrial carbon emissions. Short-term carbon emission forecasting is crucial for dynamic regulatory oversight and policy response. Using panel data from 30 Chinese provinces spanning 2019–2023, this study employs the STIRPAT model to examine the effects of socioeconomic factors on China’s industrial carbon emissions. By aligning data across different frequencies through discrete wavelet transformation, we constructed a dual-channel deep learning model capable of synchronously capturing short-term volatility and long-term evolutionary trends, which eables high-precision daily forecasts. The findings reveal: (1) Industrial Population and Industrial Output per Capita exhibit significant positive effects on carbon emission growth, while energy structure optimization and efficiency improvements yield significant emission reductions; (2) Industrial carbon emissions exhibit distinct cyclical patterns, characterized by monthly double-trough fluctuations and seasonal variations across the four quarters; (3) Short-term fluctuations in industrial carbon emissions across China’s 30 provinces show substantial differences, allowing classification into four typical regional categories. This study establishes a robust technical framework for daily industrial carbon emission forecasting and provides scientific basis for implementing differentiated, precision-targeted carbon management strategies.
Diesel freight transport has long been a major source of both carbon emissions and local air pollution in China, bringing considerable pressure to the environmental sustainability of the logistics sector. Although the relationship between transport emissions and economic growth has been discussed in earlier studies, the specific contribution of diesel-powered freight vehicles has not been fully addressed. This study developed a framework combining the Logarithmic Mean Divisia Index (LMDI) with the Decoupling Effort Index (DEI), in order to identify the main drivers of diesel freight vehicle emissions and assess the decoupling efforts of different decomposition effects. The analysis covered CO2 and four key air pollutants, including CO, HC, NOx, and PM2.5, across seven major regions in China. The study period spanned 2001–2020, covering four Five-Year Plan periods. Results showed that the economic growth factor was the main driver of emission increases, while changes in industrial structure and freight emission intensity played a role in slowing the growth. At the national level, the DEI results indicated a transition from no decoupling effort during 2001–2005 to weak decoupling effort during 2006–2015, followed by strong decoupling effort during 2016–2020. Across regions, clear differences were observed in both the scale of emissions and the progress of decoupling. In most areas, high freight intensity remained a major challenge, and population size had little effect on decoupling efforts. By bringing together multiple pollutants and examining both spatial and temporal variation, the study offers a way to better understand regional differences in emission patterns. The findings provide region-specific evidence for improving diesel freight emission control, optimizing freight efficiency, and supporting China’s low-carbon transport transition.
The implementation of the Fund Responding to Loss and Damage (FRLD) at COP 28 and COP 29 left the issue of providing substantial financing for addressing the needs of vulnerable countries unresolved. This original article introduces transformative perspectives on the operationalization of the FRLD, recommending the abstract technology of risk pooling to make it functional. To pinpoint a global risk pooling strategy for the FRLD, we lay out a systematic overview of various approaches based on how they spread the costs of risks between developed and developing countries. This typology encompasses a broad spectrum of global risk pooling strategies of different normative natures (mutuality, solidarity, and strict liability) and institutional arrangements (public-led and private-led public-private partnerships (PPPs)). In terms of both fairness and effectiveness, we advocate external solidarity insurance within a public-led PPP model as the most suitable risk pooling scheme for implementing the FRLD. Due to its similarity to existing risk pooling strategies and its political feasibility within the current institutional context, we argue that public external solidarity insurance on a global scale is a realistic and practical option.
The global power system is currently undergoing three fundamental transitions: decarbonization, marketization, and digitalization. These changes have transcended merely technical considerations, positioning themselves as key arenas for geopolitical competition and socio-economic restructuring. A complementary and coordinated development model among diverse power generation agents can effectively reconcile the seemingly contradictory goals of transitioning to low-carbon energy and ensuring energy security. Our study leverages Empirical Mode Decomposition to establish an analytical framework for China's power generation coordination. Resource endowments, economic growth, market structure, and climatic and environmental conditions core factors influencing the coordinated development of power generation, with market structure superseding resources as the pivotal variable. By 2030, the share of renewable energy in each Chinese province will reach 23
Managed forests, including plantation systems, play a vital and often underappreciated role in contributing to the global carbon sink and mitigating climate change, and determining the most effective mitigation strategies requires accounting methods that accurately assess the climate effects of forests. We use a dynamic life cycle assessment methodology to compare the climate effects of thirty-six forest management scenarios (varying site productivity, planting density, fertilization, and thinning) with varying rotation lengths for loblolly pine plantations in the southern U.S., including both in situ and ex situ greenhouse gas fluxes. We also evaluate the effectiveness of using only carbon stock estimates to assess the net climate effect of a given management regime relative to radiative forcing metrics. Carbon stocks alone failed to accurately portray the climate effects from different management regimes, emphasizing the need for greenhouse gas accounting methodologies that directly represent the effect of forest management on potential atmospheric warming mitigation efforts. When radiative forcing is used for comparison, our results show that management decisions such as thinning and rotation length should be adjusted based on stand-specific conditions, and that overgeneralized strategies, such as extending rotation lengths, had little effect on net radiative forcing for many scenarios.
Urban greening is a global strategic priority for promoting sustainability and resilience. While urban forestry strategies often target ambitious tree coverage (TC) goals, they frequently lack the resources and knowledge to effectively guides their realization. Taking the metropolitan area of Orlando, United States, as a case study, this research examines 2013–2021 TC dynamics and their varying impacts on thermal comfort and associated socioeconomic changes to provide unique insights into urban TC goals. Built on biennial sub-meter tree maps, Orlando experienced a net TC decline from 44.79
To address the dual objectives of energy security and carbon reduction, the Chinese government has proposed a strategy to promote high quality energy development (HED). In the digital era, HED is closely intertwined with digital infrastructure (DI). This study applies multiple analytical methods, including the entropy method, the coupling coordination degree model (CCDM), exploratory spatial data analysis (ESDA), the Dagum Gini coefficient, and the optimal parameter geographic detector (OPGD), to examine the coupling coordination degree (CCD) and its driving factors between DI and HED across 281 Chinese cities from 2006 to 2020. In addition, the development trends of CCD for the period 2025–2035 are projected. The results show that (1) the overall CCD of Chinese cities remained relatively low and exhibited minor inconsistencies by 2020. From a spatial distribution perspective, CCD values were higher in the eastern region and lower in the northeastern region. (2) CCD demonstrated a significant positive spatial autocorrelation, with high–high clusters concentrated in the Pearl River Delta and the Yangtze River Delta. (3) National CCD distribution was relatively uneven, with regional differences serving as the primary source of variation. (4) Although key driving factors varied across subregions, economic indicators consistently emerged as the dominant drivers in most spatial areas, and their effects were significantly amplified through interactions with other variables. (5) CCD is projected to improve steadily; under scenarios of increasing computing power demand and higher green power penetration, CCD is expected to reach a higher coordination level at an accelerated pace. Overall, this study provides deeper insights into the fundamental characteristics of CCD at the urban scale and offers data‑driven evidence to support more coordinated and low-carbon development pathways.
The agricultural sector plays a vital role in ensuring food security and supporting sustainable economic growth. However, climate change and global warming pose significant challenges to the long-term viability of agricultural production, underscoring the need for sustainable agricultural practices. Adopting clean technologies and environmentally responsible policies is essential for balancing economic and agricultural sustainability while minimizing ecological harm. Supportive and protective agricultural inputs are widely used to enhance productivity, yet excessive, unregulated use can lead to environmental degradation. This deterioration negatively impacts ecosystems, reducing biodiversity and straining natural resources. The Load Capacity Factor (LCF) serves as a critical metric for assessing the environmental footprint of agricultural activities. This paper investigates the effects of agricultural value-added, pesticide use, and fertilizer application on LCF in BRICS-T countries. The findings indicate that these variables exhibit long-term cointegration, with varying short- and long-term impacts. To ensure the sustainability of agricultural production while safeguarding ecosystems, it is essential to integrate clean technologies, precision farming, and regenerative agricultural practices into policy frameworks. Sustainable agricultural systems and environmentally conscious policies are fundamental to securing resilient food production and long-term economic stability.
Climate change education in universities is often presented as a key response to the climate crisis, yet less attention has been paid to how this field is communicated in scholarly texts and to the tonal and rhetorical patterns through which climate change education is framed. We analyse 777 English-language journal article abstracts on climate change and sustainability education in higher education, published between 2015 and 2024. Using DistilBERT polarity scores, TextBlob subjectivity scores, hedging and certainty markers, and a communication-style typology, we examine abstract-level rhetorical stance as a feature of scholarly communication about climate change education. We use these indicators to assess how rhetorical features vary across disciplines and national vulnerability tiers identified through a Composite Vulnerability Score based on the INFORM Risk Index and the WorldRiskIndex. Overall, abstracts are mildly positive and relatively cautious, suggesting a relatively stable rhetorical baseline. However, this pattern is uneven. Publications associated with more climate-vulnerable countries tend to adopt more factual, certain and problem-focused framings, whereas those from less vulnerable contexts more often combine positive sentiment with evaluative or prescriptive language about pedagogy and institutional change. We situate these patterns within an information landscape shaped by climate change mis- and disinformation, highlighting that abstract-level tone and rhetorical stance can influence how climate change education scholarship is interpreted, trusted and circulated across disciplines and vulnerability contexts.
Ethiopia’s agricultural sector, predominantly rain-fed and dominated by smallholder farming, is highly vulnerable to the impacts of climate change, including erratic rainfall, rising temperatures, and recurrent droughts, which significantly undermine food security and household resilience. Climate-Smart Agriculture (CSA) has emerged as a promising solution that integrates productivity, climate adaptation, and environmental sustainability. Thus, this review contributes by systematically linking CSA practices with resilience capacities (absorptive, adaptive, transformative), which has been underexplored in the Ethiopian context to achieve food security. Using the PRISMA approach, 115 published articles between 2013 and 2025 were initially reviewed to assess the effectiveness of CSA practices, their adoption barriers, and institutional responses. The analysis is structured around three resilience dimensions, absorptive, adaptive, and transformative capacities, based on the TANGO framework. The results reveal that several climate-smart agricultural practices in Ethiopia are improving household resilience and food security outcomes. The key practices are soil and water conservation, agroforestry, crop diversification, conservation agriculture, climate information services, improved crop and livestock varieties, integrated soil fertility management, and water harvesting and irrigation systems. These interventions contribute to improved agricultural productivity, livelihood diversification, and enhanced household food security across food availability, access, utilization, and stability dimensions. Evidence further indicates that CSA strengthens household resilience through absorptive, adaptive, and transformative capacities by improving coping mechanisms, enhancing adaptive decision-making, and supporting long-term livelihood transformation. CSA adoption, however, is shaped by demographic, socioeconomic, institutional, agro-ecological, and sociocultural factors. Despite substantial opportunities, including indigenous knowledge systems, expanding farmer networks, and climate information services, implementation remains constrained by weak institutional coordination, financial limitations, inadequate extension support, and uneven access to information and resources. Therefore, the review demonstrates that CSA serves as an important pathway for strengthening resilience and improving food security in Ethiopia; however, scaling its benefits requires integrated policies, strengthened institutions, and context-specific interventions that address local realities and inequalities.
Brazil ranks among the highest emitters of greenhouse gases (GHG) globally, which emphasizes the relevance of its contribution to the Paris Agreement goals. This study analyzes trends in gross GHG emissions across Brazil from 2000 to 2023, aggregated by region and economic sector, to assess the country’s performance regarding emissions over recent decades. The historical emissions data, expressed in carbon dioxide equivalent (CO2-eq), were sourced from the System for Estimating Greenhouse Gas Emissions (SEEG), an initiative created by a Brazilian NGO. Data analysis was conducted using R (version 4.4.1) and RStudio, focusing on the Mann-Kendall and Sen’s Slope methods applied to the 27 Brazilian states. Our findings indicate that only 1 state demonstrated a reduction throughout 2000 to 2023; 44
Forests are central to global mitigation strategies, yet efforts to advance forest‑based climate action remain contentious and slow. Mainstream assessments tend to focus on technical, economic, and institutional barriers but often overlook the political and normative dimensions of why progress stalls. This paper examines how forest and climate experts across different regions and sectors appraise challenges to forest-based climate mitigation, where they align or disagree, and which barriers are seen as most impactful and feasible to address. Using Group Concept Mapping, the study surfaces contestation over how challenges are framed and whose knowledge and interests are reflected in forest-climate decisions. Expert ratings show broad convergence around the importance of challenges related to finance, governance, Indigenous Peoples’ and local communities’ rights, and accounting for multiple forest values. Assessments diverged more on carbon accounting, offsetting, the power and influence of different actors and forest management approaches. Critically, the analysis reveals a core tension in forest-climate governance: barriers perceived as most consequential are seen as the least feasible to overcome, calling into question whether current governance systems are fit for purpose. Only a small set of challenges, mostly pertaining to epistemic governance issues, are viewed as both relatively impactful and feasible to tackle. These findings suggest that advancing forest-based mitigation will require addressing shared and tractable priorities in the near term, while confronting the deeper governance and political constraints that currently limit action on the most critical barriers.
While all humans are expected to be affected by climate change in some way, farmers in less developed countries are especially vulnerable due to high dependence on agriculture and lack of resources to cope with climate-related events. In the last few years, traditional knowledge has gained popularity among researchers and practitioners due to its adaptability, resilience and potential to promote climate change adaptation from the grassroots level. Nevertheless, little research has been conducted to determine the socioeconomic factors shaping decisions of adopting traditional technologies over other alternatives. With a large data set (n = 5,324) covering diverse ethnicities and geographical regions, this paper analyzes the determinants of the choice of climate change adaptation technologies in Ecuador. The findings show that around half of the farmers having been affected by climate change have adopted adaptation responses, with most of them relying on traditional adaptation technologies. The results of a multinomial probit regression reveal that the likelihood of adopting traditional technologies is higher for indigenous farmers in areas not accessible by road, but is lower for farmers that have received training from governmental organizations. The likelihood of adopting modern adaptation technologies is positively correlated with access to credit and access to training, regardless if it is delivered by the Government, private companies or NGOs. Farmers in the Amazon are less likely to adopt any climate change adaptation measure. Incorporating traditional knowledge to training programs, increasing availability of training programs and providing financial mechanisms for traditional farmers willing to incorporate modern technologies to their adaptation strategies are explored as alternatives to strengthen climate change adaptation among Ecuadorian farmers.
Green finance is increasingly viewed as a key instrument for achieving low-carbon development, yet its empirical effectiveness remains underexplored. This study assesses the impact of China’s Green Financial Reform and Innovation Pilot Zones (GFRIPZ) on regional low-carbon development. Using province-level panel data from 2011 to 2020 and a synthetic control method, we construct counterfactual scenarios to identify causal policy effects. The results show that GFRIPZ implementation significantly enhances low-carbon development performance. This improvement is driven by a transition toward cleaner production and more efficient allocation of financial resources to low-carbon sectors. Heterogeneity analysis indicates that policy effects vary with regional economic development, industrial structure, financial maturity, and environmental capacity. These findings provide robust evidence that financial reform can serve as an effective climate mitigation strategy and offer policy implications for developing economies seeking sustainable growth pathways.
The transition toward clean energy is central to achieving sustainable development, yet its localized health co-benefits remain largely underexplored in fossil-fuel-dependent economies, particularly concerning vulnerable populations such as young children. This paper investigates the effects of renewable energy deployment on child health outcomes by leveraging a comprehensive, unbalanced panel dataset of 2,780 Chinese counties spanning the years 2000 to 2020. Employing a population-weighted least squares (WLS) empirical strategy to account for regional heterogeneity, we document a robust protective effect that, an additional 1
