Search Results (133)

Against the backdrop of increasing extreme weather events associated with global climate change, regulating carbon dioxide emissions, a primary contributor to atmospheric warming, has emerged as a pressing global challenge. Focusing on China as a representative case study of major developing economies, this research examines industrial carbon emission patterns during 2001–2022. Methodologically, it introduces an innovative analytical framework that integrates the Generalized Divisia Index Method (GDIM) with the Low Emissions Analysis Platform (LEAP) to both decompose industrial emission drivers and project future trajectories through 2040. Key findings reveal that:the following: (1) Carbon intensity in China’s industrial sector has been substantially decreasing under green technological advancements and policy interventions. (2) Industrial restructuring demonstrates constraining effects on carbon output, while productivity gains show untapped potential for emission abatement. Notably, the dual mechanisms of enhanced energy efficiency and cleaner energy transitions emerge as pivotal mitigation levers. (3) Scenario analyses indicate that coordinated policies addressing energy mix optimization, efficiency gains, and economic restructuring could facilitate achieving industrial carbon peaking before 2030. These results offer substantive insights for designing phased decarbonization roadmaps, while contributing empirical evidence to international climate policy discourse. The integrated methodology also presents a transferable analytical paradigm for emission studies in other industrializing economies. Full article

Against the backdrop of increasingly severe global climate change, the automotive industry, as a carbon-intensive sector, has found its low-carbon transformation crucial for achieving the “double carbon” goals. This paper constructs manufacturer decision-making models under an oligopolistic market scenario for the single dual-credit policy and the “dual-credit + carbon cap-and-trade” policy, revealing the nonlinear impacts of new energy vehicle (NEV) credit trading prices, carbon trading prices, and credit ratio requirements on manufacturers’ pricing, emission reduction effort levels, and profits. The results indicate the following: (1) Under the “carbon cap-and-trade + dual-credit” policy, manufacturers can balance emission reduction costs and NEV production via the carbon trading market to maximize profits, with lower emission reduction effort levels than under the single dual-credit policy. (2) A rise in credit trading prices prompts hybrid manufacturers (producing both fuel vehicles and NEVs) to increase NEV production and reduce fuel vehicle output; higher NEV credit ratio requirements raise fuel vehicle production costs and prices, suppressing consumer demand. (3) An increase in carbon trading prices raises production costs for both fuel vehicles and NEVs, leading to decreased market demand; hybrid manufacturers reduce emission reduction efforts, while others transfer costs through price hikes to boost profits. (4) Hybrid manufacturers face high carbon emission costs due to excessive actual fuel consumption, driving them to enhance emission reduction efforts and promote low-carbon technological innovation. Full article

Against the backdrop of evolving global climate patterns, the frequency and intensity of extreme weather events have increased significantly, posing unprecedented threats to agricultural production. This change has particularly profound impacts on agricultural systems in developing countries, making the enhancement of farmers’ capacity to withstand extreme weather events a crucial component for achieving sustainable agricultural development. As an essential safeguard for agricultural production, agricultural insurance plays an indispensable role in risk management. However, a pronounced gap persists between policy aspirations and actual adoption rates among farmers in developing economies. This study employs the integrated theory of planned behavior (TPB) and protection motivation theory (PMT) to construct an analytical framework incorporating psychological, socio-cultural, and risk-perception factors. Using Jiangsu Province—a representative high-risk agricultural region in China—as a case study, we administered 608 structured questionnaires to farmers. Structural equation modeling was applied to identify determinants influencing insurance adoption decisions. The findings reveal that farmers’ agricultural insurance purchase decisions are influenced by multiple factors. At the individual level, risk perception promotes purchase intention by activating protection motivation, while cost–benefit assessment enables farmers to make rational evaluations. At the social level, subjective norms can significantly enhance farmers’ purchase intention. Further analysis indicates that perceived severity indirectly enhances purchase intention by positively influencing attitude, while response costs negatively affect purchase intention by weakening perceived behavior control. Although challenges such as cognitive gaps and product mismatch exist in the intention-behavior transition, institutional trust can effectively mitigate these issues. It not only strengthens the positive impact of psychological factors on purchase intention, but also significantly facilitates the transformation of purchase intention into actual behavior. To promote targeted policy interventions for agricultural insurance, we propose corresponding policy recommendations from the perspective of public intervention based on the research findings. Full article

Against the backdrop of global climate change and rapid urbanization, understanding the spatiotemporal dynamics and driving mechanisms of vegetation net primary productivity (NPP) is critical for ensuring regional ecological security and achieving carbon neutrality goals. This study focuses on the Yangtze River Delta Urban Agglomeration (YRDUA) and integrates multi-source remote sensing data with socioeconomic statistics. By combining interpretable machine learning (XGBoost-SHAP) with multiscale geographically weighted regression (MGWR), and incorporating Theil–Sen trend analysis and Mann–Kendall significance testing, we systematically analyze the spatiotemporal variations in NPP and its multiscale driving mechanisms from 2001 to 2020. The results reveal the following: (1) Total NPP in the YRDUA shows an increasing trend, with approximately 24.83% of the region experiencing a significant rise and only 2.75% showing a significant decline, indicating continuous improvement in regional ecological conditions. (2) Land use change resulted in a net NPP loss of 2.67 TgC, yet ecological restoration and advances in agricultural technology effectively mitigated negative impacts and became the main contributors to NPP growth. (3) The results from XGBoost and MGWR are complementary, highlighting the scale-dependent effects of driving factors—at the regional scale, natural factors such as elevation (DEM), precipitation (PRE), and vegetation cover (VFC) have positive impacts on NPP, while the human footprint (HF) generally exerts a negative effect. However, in certain areas, a dose–response effect is observed, in which moderate human intervention can enhance ecological functions. (4) The spatial heterogeneity of NPP is mainly driven by nonlinear interactions between natural and anthropogenic factors. Notably, the interaction between DEM and climatic variables exhibits threshold responses and a “spatial gradient–factor interaction” mechanism, where the same driver may have opposite effects under different geomorphic conditions. Therefore, a well-balanced combination of land use transformation and ecological conservation policies is crucial for enhancing regional ecological functions and NPP. These findings provide scientific support for ecological management and the formulation of sustainable development strategies in urban agglomerations. Full article

Against the backdrop of global climate change, enhancing farmers’ adaptive capacity to reduce crop production risks has emerged as a critical concern for governments and researchers worldwide. Drawing on social capital theory, this study develops a four-dimensional measurement framework comprising social networks, social trust, social norms, and social participation, utilizing survey data from 1772 households in the Yellow River Basin. We employ factor analysis to construct comprehensive social capital scores and apply ordered Probit models to examine how social capital influences farmers’ climate adaptation behaviors, with particular attention to the moderating roles of agricultural extension interaction and digital literacy. Key findings include: (1) Adoption patterns: Climate adaptation behavior adoption remains low (60%), with technical adaptation measures showing particularly poor uptake (13%); (2) Direct effects: Social capital significantly promotes adaptation behaviors, with social trust (p < 0.01), networks (p < 0.01), and participation (p < 0.05) demonstrating positive effects, while social norms show no significant impact; (3) Heterogeneous effects: Impact mechanisms differ by crop type, with grain producers relying more heavily on social networks (+, p < 0.01) and cash crop producers depending more on social trust (+, p < 0.01); (4) Moderating mechanisms: Agricultural extension interaction exhibits scale-dependent effects, negatively moderating the relationship for large-scale farmers (p < 0.05) while showing no significant effects for smaller operations; digital literacy consistently demonstrates negative moderation, whereby higher literacy levels weaken social capital’s promotional effects (p < 0.01). Policy recommendations: Effective climate adaptation strategies should integrate strengthened rural social organization development, differentiated agricultural extension systems tailored to farm characteristics, and enhanced rural digital infrastructure investment. Full article

This study systematically reviews the development and application of remote sensing technology in monitoring and evaluating urban heat island (UHI) effects. The urban heat island effect, characterized by significantly higher temperatures in urban areas compared to surrounding rural regions, has become a widespread environmental issue globally, with impacts spanning public health, energy consumption, ecosystems, and social equity. The paper first analyzes the formation mechanisms and impacts of urban heat islands, then traces the evolution of remote sensing technology from early traditional platforms such as Landsat and NOAA-AVHRR to modern next-generation systems, including the Sentinel series and ECOSTRESS, emphasizing improvements in spatial and temporal resolution and their application value. At the methodological level, the study systematically evaluates core algorithms for land surface temperature extraction and heat island intensity calculation, compares innovative developments in multi-source remote sensing data integration and fusion techniques, and establishes a framework for accuracy assessment and validation. Through analyzing the heat island differences between metropolitan areas and small–medium cities, the relationship between urban morphology and thermal environment, and regional specificity and global universal patterns, this study revealed that the proportion of impervious surfaces is the primary driving factor of heat island intensity while simultaneously finding that vegetation cover exhibits significant cooling effects under suitable conditions, with the intensity varying significantly depending on vegetation types, management levels, and climatic conditions. In terms of applications, the paper elaborates on the practical value of remote sensing technology in identifying thermally vulnerable areas, green space planning, urban material optimization, and decision support for UHI mitigation. Finally, in light of current technological limitations, the study anticipates the application prospects of artificial intelligence and emerging analytical methods, as well as trends in urban heat island monitoring against the backdrop of climate change. The research findings not only enrich the theoretical framework of urban climatology but also provide a scientific basis for urban planners, contributing to the development of more effective UHI mitigation strategies and enhanced urban climate resilience. Full article

Outbreaks of locust plagues are becoming increasingly frequent against the backdrop of climate change. Locust outbreaks in the Caucasus and Central Asia, especially in Kazakhstan, pose continuous threats to neighboring countries, including China, Kyrgyzstan, and more. However, locust outbreak forecasts and migration movement are yet to be studied in this area. In our study, we collected water level data in major lakes and water bodies, as well as annual average precipitation in the past 15 years in Kazakhstan, to analyze their contributions to locust outbreaks. Multiple linear regression analysis revealed a significant negative correlation between overall lake water level and the following year’s locust outbreak area in Kazakhstan. Considering that the overall lake water levels in 2023 and 2024 reached a quite low level historically, we predicted heavy locust outbreaks in 2025. Furthermore, through wind field analysis and wind-born trajectory modeling, we identified two migration routes of locusts from Kazakhstan into Xinjiang, China, riding the northwest wind, with lakes near the Sino-Kazakhstan border as the main sources. Overall, our study identified high locust outbreak challenges in Kazakhstan in recent years and determined two wind-supported migration routes of locusts invading China, which are significant for guiding monitoring and prevention efforts in the Sino-Kazakhstan border area. Full article

Against the backdrop of global climate change and increasing ecological pressure, the refined monitoring of forest resources and accurate tree species identification have become essential tasks for sustainable forest management. Hyperspectral remote sensing, with its high spectral resolution, shows great promise in tree species classification. However, traditional methods face limitations in extracting joint spatial–spectral features, particularly in complex forest environments, due to the “curse of dimensionality” and the scarcity of labeled samples. To address these challenges, this study proposes a synergistic classification approach that combines the spatial feature extraction capabilities of deep learning with the generalization advantages of machine learning. Specifically, a 2D convolutional neural network (2DCNN) is integrated with a support vector machine (SVM) classifier to enhance classification accuracy and model robustness under limited sample conditions. Using UAV-based hyperspectral imagery collected from a typical plantation area in Fuzhou City, Jiangxi Province, and ground-truth data for labeling, a highly imbalanced sample split strategy (1:99) is adopted. The 2DCNN is further evaluated in conjunction with six classifiers—CatBoost, decision tree (DT), k-nearest neighbors (KNN), LightGBM, random forest (RF), and SVM—for comparison. The 2DCNN-SVM combination is identified as the optimal model. In the classification of Masson pine, Chinese fir, and eucalyptus, this method achieves an overall accuracy (OA) of 97.56%, average accuracy (AA) of 97.47%, and a Kappa coefficient of 0.9665, significantly outperforming traditional approaches. The results demonstrate that the 2DCNN-SVM model offers superior feature representation and generalization capabilities in high-dimensional, small-sample scenarios, markedly improving tree species classification accuracy in complex forest settings. This study validates the model’s potential for application in small-sample forest remote sensing and provides theoretical support and technical guidance for high-precision tree species identification and dynamic forest monitoring. Full article

Against the backdrop of intensifying global climate change and deepening sustainable development goals, the low-carbon transformation of agriculture, as a major greenhouse gas emission source, holds significant strategic importance for achieving China’s “carbon peaking and carbon neutrality” (referred to as the “dual carbon”) targets. To reveal the spatiotemporal evolution characteristics and complex driving mechanisms of agricultural carbon emissions (ACEs), this study constructs a comprehensive accounting framework for agricultural carbon emissions based on provincial panel data from China spanning 2000 to 2023. The framework encompasses three major carbon sources—cropland use, rice cultivation, and livestock farming—enabling precise quantification of total agricultural carbon emissions. Furthermore, spatial-temporal distribution patterns are characterized using methodologies including standard deviational ellipse (SDE) and spatial autocorrelation analysis. For driving mechanism identification, the Geodetector and Geographically and Temporally Weighted Regression (GTWR) models are employed. The former quantifies the spatial explanatory power and interaction effects of driving factors, while the latter enables dynamic estimation of factor influence intensities across temporal and spatial dimensions, jointly revealing significant spatiotemporal heterogeneity in driving mechanisms. Key findings: (1) temporally, total ACEs exhibit fluctuating growth, while emission intensity has significantly decreased, indicating the combined effects of policy regulation and technological advancements; (2) spatially, emissions display an “east-high, west-low” pattern, with an increasing number of hotspot areas and a continuous shift of the emission centroid toward the northwest; and (3) mechanistically, agricultural gross output value is the primary driving factor, with its influence fluctuating in response to economic and policy changes. The interactions among multiple factors evolve over time, transitioning from economy-driven to synergistic effects of technology and climate. The GTWR model further reveals the spatial and temporal variations in the impacts of each factor. This study recommends formulating differentiated low-carbon agricultural policies based on regional characteristics, optimizing industrial structures, enhancing modernization levels, strengthening regional collaborative governance, and promoting the synergistic development of climate and agriculture. These measures provide a scientific basis and policy reference for achieving the “dual carbon” goals. Full article

Against the backdrop of a growing global population and intensifying climate change, crop pests and diseases have become significant challenges affecting agricultural production and food security. Efficient and precise detection and control of crop pests and diseases are crucial for ensuring yield and quality, reducing agricultural losses, and promoting sustainable agriculture. In recent years, intelligent diagnostic methods based on machine learning and deep learning have advanced rapidly, providing new technological means for the early detection and management of crop pests and diseases. Meanwhile, large language models have demonstrated potential advantages in information integration and knowledge inference, offering prospects for more scientific and efficient decision support in pest and disease control. This paper reviews the research progress in the application of machine learning, deep learning, and large language models in crop pest and disease detection and control, analyzes the challenges in current technological implementations, and explores future development directions. Full article

Global food security is facing numerous severe challenges. Population growth, climate change, and irrational agricultural inputs have led to a reduction in available arable land, a decline in soil fertility, and difficulties in increasing crop yields. As a result, the supply of food and agricultural products is under serious threat. Against this backdrop, the development of new technologies to increase the production of food and agricultural products and ensure their supply is extremely urgent. Agricultural nanotechnology, as an emerging technology, mainly utilizes the characteristics of nanomaterials such as small size, large specific surface area, and surface effects. It plays a role in gene delivery, regulating crop growth, adsorbing environmental pollutants, detecting the quality of agricultural products, and preserving fruits and vegetables, providing important technical support for ensuring the global supply of food and agricultural products. Currently, the research focus of agricultural nanotechnology is concentrated on the design and preparation of nanomaterials, the regulation of their properties, and the optimization of their application effects in the agricultural field. In terms of the research status, certain progress has been made in the research of nano-fertilizers, nano-pesticides, nano-sensors, nano-preservation materials, and nano-gene delivery vectors. However, it also faces problems such as complex processes and incomplete safety evaluations. This review focuses on the horticultural industry, comprehensively expounding the research status and application progress of agricultural nanotechnology in aspects such as the growth regulation of horticultural crops and the quality detection and preservation of horticultural products. It also deeply analyzes the opportunities and challenges faced by the application of nanomaterials in the horticultural field. The aim is to provide a reference for the further development of agricultural nanotechnology in the horticultural industry, promote its broader and more efficient application, contribute to solving the global food security problem, and achieve sustainable agricultural development. Full article

In the global pursuit of sustainable development and climate change mitigation, reconciling export growth with environmental protection has emerged as a universal challenge. As the world’s largest developing economy, China has traditionally relied on a resource-intensive development model to fuel rapid foreign trade growth. However, this extensive growth pattern has not only led to environmental pollution domestically but has also encountered hurdles from international green trade barriers. Finance, as a key driver of stable economic growth, plays a pivotal role in achieving high-quality trade development. Against this backdrop, the Chinese government has introduced the green credit interest subsidies policy. This policy aims to coordinate government financial resources and guide capital toward green production, alleviating financing constraints and fostering the upgrading of export product quality. Utilizing data from the World Bank, China Customs statistics, and provincial panels from 2011 to 2020, this study employs a multi-period difference-in-differences (DID) model to examine the causal impact of the green credit subsidies policy on efforts to upgrade the export quality of green products across China’s regions. The benchmark regression results indicate that the green credit interest subsidies policy has significantly improved the export quality of green products across China’s manufacturing industries. Heterogeneity analysis shows that this policy has had a more pronounced positive impact on green product quality in industries with quality-based competition strategies, in regions with well-coordinated local finance and financial policies, as well as in countries that have concluded environmental clauses with China. Mechanism analysis reveals that, on the export side, the policy enhances green product quality by easing financing constraints, increasing green credit, boosting productivity, and upgrading industrial structures. On the import side, the policy promotes green product quality by expanding the scale, variety, and quality of green intermediate goods. This research offers valuable insights for developing countries aiming to establish export-oriented green transformation and upgrading strategies. Full article

Against the backdrop of global energy crises and climate change, the iron and steel industry, as a typical high energy consumption and high-emission sector, faces rigid constraints for energy conservation and emission reduction. This paper systematically reviews the research progress and application effects of energy-saving technologies across the entire steel production chain, including coking, sintering, ironmaking, steelmaking, continuous casting, and rolling processes. Studies reveal that technologies such as coal moisture control (CMC) and coke dry quenching (CDQ) significantly improve energy utilization efficiency in the coking process. In sintering, thick-layer sintering and flue gas recirculation (FGR) technologies reduce fuel consumption while enhancing sintered ore performance. In ironmaking, high-efficiency pulverized coal injection (PCI) and hydrogen-based fuel injection effectively lower coke ratios and carbon emissions. Integrated and intelligent innovations in continuous casting and rolling processes (e.g., endless strip production, ESP) substantially reduce energy consumption. Furthermore, the system energy conservation theory, through energy cascade utilization and full-process optimization, drives dual reductions in comprehensive energy consumption and carbon emission intensity. The study emphasizes that future advancements must integrate hydrogen metallurgy, digitalization, and multi-energy synergy to steer the industry toward green, high-efficiency, and low-carbon transformation, providing technical support for China’s “Dual Carbon” goals. Full article

Natural gas (NG) is a key transitional energy source for clean energy transition. Against the backdrop of a grim climate change situation, the sustainable development of the Chinese NG industry is emphasized. Climate change disclosure (CCD) has become an important way for corporations to fulfill their social responsibility and demonstrate their capacity for sustainable development. In order to understand the current status of CCD in the Chinese NG industry and to improve the deficiencies, this paper assesses the quality of CCD in the Chinese NG industry. Climate change information is not fully covered by the existing quality evaluation systems. This study establishes a highly applicable system for evaluating the quality of CCD based on the theory pillar perspective. It includes the following five dimensions: completeness, balance, reliability, comparability, and understandability. This study evaluates the quality of CCD of 58 NG corporations using content analysis and quality evaluation index methods, incorporating Skip-Gram and CRITIC models. The evaluation results indicate that the quality of climate reports in the Chinese NG industry has shown general improvement over time; however, inconsistencies remain, making comparisons challenging. There are differences in the level of quality of CCD in the Chinese NG industry. Policy incentives with clear guidance and regional economic development conditions have a notable impact on the quality of CCD. For Chinese NG corporations themselves, disclosing climate change information related to risk management is the focus of narrowing the reporting gap. The CCD quality evaluation system constructed in this paper provides a theoretical reference for all industries to accurately promote disclosure quality. It also provides practical guidelines for corporations to identify weak links in CCD. Full article

Against the backdrop of global climate change and rapid urbanisation, carbon-neutral urban governance and sustainable urban development have become core issues of concern to the international community. As the world’s largest carbon emitter, Chinese cities shoulder the significant responsibility of achieving the “dual-carbon” goal. This study utilised a unique panel dataset of 300 cities in China from 2015 to 2022 and proposed a multi-dimensional analytical framework from the perspective of environmental sociology. This paper empirically examines the impact mechanism of carbon-neutral governance on urban sustainable development and its regional heterogeneity by using this framework. The research findings are as follows: First, carbon-neutral governance has a significant promoting effect on the sustainable development of cities. Secondly, technological input (the number of scientific researchers) plays a significant mediating role between carbon-neutral governance and sustainable development, indicating that technology diffusion is an important way for the transmission of policy effects. Thirdly, the analysis of regional heterogeneity indicates that due to policy inclination and resource concentration, western cities contribute the most to sustainable development, followed by eastern cities, and central cities contribute the least to sustainable development. The eastern region was identified as the second weakest and the central region as the weakest. This research provides theoretical and empirical basis for differentiated formulation of carbon neutrality policies, strengthening scientific and technological support, and optimising regional collaborative governance. Full article