Search Results (146)

For less-developed, carbon-dependent regions, achieving carbon decoupling while pursuing economic catch-up presents a fundamental challenge. This study investigates this persistent dilemma through the case of Jiangxi Province, China, a typical coal-reliant inland region. Utilizing data from 2000 to 2022, we estimate carbon emissions following IPCC guidelines and employ the Generalized Divisia Index Method (GDIM) to decompose emission drivers, effectively overcoming the limitation of factor independence in conventional decomposition analyses. The results identify economic scale (cumulative contribution: 97.81%) and energy consumption (51%) as the primary drivers of emission growth, while carbon intensity of output (−47.38%) emerges as the strongest inhibiting factor. The application of the Tapio decoupling model reveals that weak decoupling is the dominant state, prevailing in 91% of the study period. This persistent pattern underscores only a partial and unstable separation between economic growth and emissions, highlighting the region’s entrenched carbon lock-in. Our findings demonstrate that transcending this weak decoupling dilemma necessitates a strategic shift beyond efficiency gains. We propose that the resolution lies in accelerating structural transitions within the energy system and fostering low-carbon industrial upgrading. This study not only elucidates the dynamics of the carbon decoupling challenge in catch-up regions but also offers actionable and context-specific pathways, providing a valuable reference for analogous regions, particularly in developing and transition economies. Full article

This research focuses on Henan, a key agricultural region, analyzing data from 2000 to 2022 on cropland use and agricultural input–output. It employs the Tapio decoupling model to examine the evolution and decoupling of cropland use intensity (CLUI) and cropland use carbon emissions (CUCE) in the province. The study reveals that from 2000 to 2022, CLUI in Henan Province fluctuated in a “high-low-high” pattern over time, creating a spatial distribution with high-intensity areas in the east and lower-intensity areas at the provincial boundaries. CUCE showed a “U” shaped trend, peaking around 2015 and then gradually declining. Spatially, emissions were consistently higher in the south and lower in the north. The relationship between CLUI and CUCE transitioned from a strong negative decoupling from 2000 to 2010, to a strong decoupling from 2015 to 2020, and to a recessive decoupling from 2020 to 2022. Spatially, it evolves from a state of negative decoupling across the entire region in the early stage to nearly full coverage of strong decoupling regions in the later stage. Based on these insights, the study suggests planning strategies focusing on regional management and policy alignment, providing scientific guidance for sustainable cropland use and optimized territorial planning in Henan Province. Full article

This study examines the relationship between energy consumption and energy-related CO₂ emissions for a sample of 79 reporting entities, grouped into seven regions, over the period 2013–2023. The methodology uses three empirical tools: (i) Tapio elasticity to classify types of decoupling; (ii) Kaya–LMDI decomposition to identify factors that determine emissions; and (iii) a log-difference panel model to separate year- and country-specific effects. The results indicate a reduction in carbon intensity in all regions, more pronounced in Europe and North America. According to the Tapio classification, Europe is in recessive decoupling, the Middle East is on the verge of expansive decoupling, North and South America are in strong expansive decoupling, and Asia Pacific, Africa, and CIS show only weak signals of expansive decoupling. The LMDI results show that, in regions with strong decoupling, the decrease in carbon intensity contributes to reducing emissions. In those with weak decoupling, the effects are partially canceled out by population growth and energy demand. Finally, the fixed-effects panel model does not identify any structural decoupling at the regional level. Overall, this study contributes to the literature by separating long-term structural effects from annual fluctuations. On this basis, we provide clear guidelines for designing regional energy policies. Full article

Agricultural non-point source pollution (ANPSP) represents a major threat to water quality, yet its spatiotemporal dynamics in arid and semi-arid regions remain poorly quantified. This study establishes an integrated assessment framework to analyze the spatiotemporal patterns and driving mechanisms of ANPSP in Inner Mongolia, China, from 2002 to 2023. Using a combination of inventory analysis, pollution load equivalence assessment, and the Tapio decoupling model, we systematically examined the evolution of four pollution sources—chemical fertilizers, livestock breeding, agricultural solid waste, and rural domestic discharge—across 12 administrative regions. These methods were sequentially applied to quantify loads, standardize impacts, and evaluate the economy–environment relationship, forming a coherent analytical chain. Key results indicate the following: (1) Pollutant loads increased consistently over the study period, with chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) rising by 24.21%, 31.67%, and 31.14%, respectively, largely driven by livestock sector expansion. (2) Spatial distribution was highly heterogeneous, with Tongliao, Chifeng, and Hulunbuir contributing 50.58–58.31% of total emissions, in contrast to minimal impacts in western regions. (3) Decoupling analysis indicated variable environment–economy relations, where fertilizer use and grain output reached strong decoupling in 2010–2011 and 2018–2019, whereas livestock pollution exhibited more unstable decoupling trajectories. A cluster-derived risk zoning scheme identified Bayannur as the only high-risk area and highlighted the need for tailored management approaches in medium- and low-risk zones. This study offers a scientific foundation for targeted ANPSP mitigation and sustainable agricultural strategy formulation in ecologically vulnerable areas. Full article

Achieving decoupling between economic growth and carbon emissions is imperative for global sustainable development. This study provides a comparative analysis of this decoupling process in the European Union (EU) and BRICS countries from 1996 to 2023, employing the Tapio decoupling model and Logarithmic Mean Divisia Index (LMDI) decomposition analysis. Our findings reveal a stark contrast: the EU has achieved an average annual carbon emission growth rate of −1%, predominantly characterized by strong decoupling, whereas the BRICS nations exhibit an average growth rate of 6.26%, mainly in a state of weak decoupling. The LMDI results indicate that the intensity effect is the primary driver of carbon reduction in the EU, while the income effect is the most significant factor promoting emissions growth in the BRICS bloc. A novel finding is the identification of a near-symmetrical relationship between the energy transition effect and the fossil energy structure effect in the cumulative decomposition charts, offering a new perspective for evaluating energy system changes. The study concludes that while the EU demonstrates a more advanced decoupling pathway, significant internal disparities persist. For BRICS countries, mitigating the pressure from economic and population growth through industrial upgrading, differentiated energy policies, and enhanced renewable infrastructure is crucial. These insights provide valuable policy implications for both developed and developing economies in navigating their low-carbon transitions. Full article

Land use change is a key driver of regional carbon emissions. Understanding the mechanisms through which regional land use changes influence carbon emissions, as well as their spatiotemporal evolution, is of great significance for the optimization of land use structure and the formulation of low-carbon policies. This study, based on land use data and socio-economic data from 2002 to 2022, combines decoupling analysis models with carbon carrying capacity assessment frameworks to systematically analyze the dynamic evolution of carbon emissions from land use in Jilin Province. The results show the following: (1) From 2002 to 2022, the cultivated land area in Jilin Province remained stable and accounted for the largest proportion; the areas of water bodies and construction land expanded, while forest, grassland, and unutilized land continued to decline. (2) Total carbon emissions exhibited a “growth-stabilization-slight decline” trend, with construction land contributing the most to emissions. Spatially, carbon emissions were concentrated in the central region with Changchun at its core. (3) The overall carbon ecological carrying capacity of Jilin Province showed a fluctuating upward trend, with notable differences in carbon ecological carrying capacity across cities. (4) Cultivated land showed the highest correlation with carbon emissions, followed by woodland. The decoupling relationship between carbon emissions and economic development exhibited phase fluctuations, evolving from weak decoupling to strong decoupling and then transitioning back to weak negative decoupling. Therefore, it is recommended that effective measures be adopted to curb the excessive expansion of construction land, enhance ecological carbon sink functions, and facilitate the transformation of cultivated land from a carbon source to a carbon sink. This will promote the efficient and green utilization of land resources, advance the synergistic progress of economic development and environmental protection, and achieve the goal of regional sustainable development. Full article

Agricultural chemicals are indispensable in the process of traditional grain production and are also a major contributor to agricultural carbon emissions. Exploring the relationship between agricultural chemical carbon emissions and grain production is of significant importance for reducing agricultural emissions and promoting environmentally friendly grain production. To this end, this study employs the Tapio model and the LMDI factor decomposition model to analyze the decoupling relationship between agricultural chemical carbon emissions and grain production in Shandong Province—a typical grain-producing region in northern China—from a production perspective, focusing on the period from 2011 to 2023. The results indicate that during this period, Shandong Province achieved improvements in grain production technology, leading to a gradual improvement in the decoupling relationship between grain production and agricultural chemical carbon emissions. The factors influencing agrochemical carbon emissions during grain production initially shifted from being suppressed by output scale effects and promoted by technological effects to being suppressed by technological effects and promoted by output scale effects. Ultimately, synergistic development was achieved in Shandong Province by reducing agrochemical emissions and increasing grain production. This study provides a theoretical basis for synergistic development in agrochemical emission reduction and grain yield enhancement, while also offering a new perspective for research on reducing emissions during grain production. Full article

This study examines the dynamic decoupling relationship between energy consumption and export growth in China since its accession to the World Trade Organization (WTO) (2002–2018) by combining the noncompetitive input–output model, Tapio decoupling model, and the Logarithmic Mean Divisia Index (LMDI) model. The results reveal the substantial energy consumption generated by China’s export trade, emphasizing the urgency of reducing energy consumption in export trade for energy conservation and emissions reduction. Since its WTO accession, China has experienced sustained improvement in the energy decoupling effect during the growth of export trade, entering a period of strong decoupling from 2014 to 2018. The expanded export scale remains a major obstacle to decoupling export trade growth from energy consumption, while decreased energy intensity in exports is a significant driving force for energy decoupling, with relatively minor impact from changes in the export trade structure. By integrating non-competitive input–output modeling, Tapio decoupling analysis, and LMDI decomposition, this study develops a novel framework to investigate the structural drivers of energy–export decoupling in China from 2002 to 2018. Bridging methods from energy systems, trade economics, and policy modeling, it contributes to the field of multi-disciplinary sustainability by offering sector-level insights and decomposition-based evidence to support more efficient, equitable, and sustainable trade transitions. Full article

Using economic and water consumption data from Inner Mongolia and its 12 cities (2004–2023), this study employs the Tapio decoupling model to investigate the relationship between water consumption and economic growth. The results show a general shift from weak to strong decoupling across the region, with extreme events such as the 2020–2021 pandemic period (decoupling index, DI = 10.31) causing clear disruptions. Regional disparities followed a triple pattern: industrial areas (e.g., Ordos, Baotou) achieved strong decoupling via innovation; agricultural regions (e.g., Tongliao, Bayannur) remained in weak negative decoupling modes due to rigid water demand; and ecologically vulnerable areas (e.g., Alxa League, Xilin Gol) saw high volatility and unsustainable policy effects. Our interpretation of the three patterns highlights the need for region-specific governance. The driving mechanisms mainly include uneven adoption of water-saving technology (e.g., low drip irrigation rates in agriculture), virtual water trade shifting pressures across regions, and climate extremes worsening imbalances. Based on these findings, we recommend differentiated subsidies, regional compensation mechanisms, and adaptive policies to support sustainable water–economy coordination in arid regions. Full article

China’s rapid urbanization and ecological civilization initiatives have intensified land space governance challenges. This paper introduces a novel integrated framework to investigate the bidirectional interactions among land space conflicts (LSC), urbanization level (UL), and eco-environment level (EL) in Jiangsu Province (2000–2020). Using a combination of landscape risk indices, TOPSIS, coupling coordination, geographic detector, and Tapio decoupling models, we analyze the spatiotemporal dynamics and underlying mechanisms. Key findings show the following: LSC intensity escalated continuously, with the highest levels in Southern Jiangsu. UL grew steadily, while EL exhibited fluctuations. UL-EL coordination significantly improved, with notable spatial clustering. Decoupling analysis indicates a weakening influence of UL on LSC, but with growing pressure from the EL. Importantly, cross-system UL-EL interactions amplified LSC intensity more than internal subsystem effects. Based on coupling–decoupling patterns, cities were classified into five typologies, providing a clear basis for targeted spatial governance strategies. This research provides both a theoretical advancement and practical insights for balancing urbanization and ecological sustainability in rapidly developing regions. Full article

Land-use carbon emissions denote the release or sequestration of greenhouse gases (e.g., CO₂, N₂O) resulting from human land-use activities, with land-use changes exerting a major influence on land-use carbon emissions. Revealing the coupling mechanism between land-use changes and carbon emissions is of crucial theoretical significance for achieving “dual carbon” goals and mitigating global climate change. Based on the land-use change data of Jiangxi Province, this study explored the Spatial–temporal relationship between land-use carbon emissions and land-use changes in Jiangxi Province from 2000 to 2020 using a model of land-use dynamic degrees, a model of land-use transfer matrices, and the IPCC carbon emission accounting model. In this study, the factors influencing changes in land-use carbon emissions were comprehensively analyzed using an LMDI model and the Tapio decoupling model. The results indicated that: (1) Jiangxi Province’s land-use changes show a “two-increase, four-decrease” trend, with construction land and unused land experiencing the most significant shifts, while water, grassland, cropland, and forestland changes stayed near 1%. (2) Net land-use carbon emissions exhibit a rapid then gradual increase, with higher emissions in the north/south and lower levels in central regions. While overall land-use carbon emission intensity is declining, per capita emissions continue to rise. (3) Land-use carbon emission changes are primarily driven by emission intensity, land-use structure, efficiency, and economic level. In Jiangxi, economic growth mainly increases land-use carbon emissions, while land-use efficiency enhancement counters this trend. Jiangxi Province shows weak land-use carbon emission–economic growth decoupling, with land-use carbon emissions rising more slowly than economic growth. This study not only provides a typical case analysis and methodological framework for understanding the carbon emission effects of human–land relationships in rapidly urbanizing regions but also offers a specific scientific basis and policy insights for Jiangxi Province and other similar regions to formulate differentiated territorial spatial planning, promote ecological protection and restoration, and achieve green and low-carbon development pathways under the “dual carbon” goals. Full article

Against the backdrop of China’s “dual carbon” strategy (carbon peaking and carbon neutrality), provincial-level carbon emission research is crucial for the implementation of related policies. However, existing studies insufficiently cover the driving mechanisms and scenario prediction for energy-importing provinces. This study can provide theoretical references for similar provinces in China to conduct research on carbon dioxide emissions from energy consumption. The carbon dioxide emissions from energy consumption in Jiangsu Province between 2000 and 2023 were calculated using the carbon emission coefficient method. The Tapio decoupling index model was adopted to evaluate the decoupling relationship between economic growth and carbon dioxide emissions from energy consumption in Jiangsu. An extended STIRPAT model was established to predict carbon dioxide emissions from energy consumption in Jiangsu, and this model was applied to analyze the emissions under three scenarios (baseline scenario, low-carbon scenario, and enhanced low-carbon scenario) during 2024–2030. The results show the following: (1) During 2000–2023, the carbon dioxide emissions from energy consumption in Jiangsu Province ranged from 215.22428 million tons to 783.94270 million tons, with an average of 549.96280 million tons. (2) The decoupling status between carbon dioxide emissions from energy consumption and economic development in Jiangsu was dominated by weak decoupling, accounting for 91.304%, while a small proportion (8.696%) of expansive coupling was also observed. (3) Under the baseline scenario, the carbon dioxide emissions from energy consumption in Jiangsu in 2030 will reach 796.828 million tons; under the low-carbon scenario, the emissions will be 786.355 million tons; and under the enhanced low-carbon scenario, the emissions will be 772.293 million tons. Furthermore, countermeasures and suggestions for reducing carbon dioxide emissions from energy consumption in Jiangsu are proposed, mainly including strengthening the guidance of policies and institutional systems, optimizing the energy consumption structure, intensifying technological innovation efforts, and enhancing government promotion and publicity. Full article

The transportation sector is crucial for achieving China’s “dual carbon” strategic goals, yet its emission drivers and decoupling mechanisms exhibit significant provincial heterogeneity that remains underexplored. Existing studies predominantly rely on the LMDI method, which suffers from limitations in handling multiple absolute indicators, and rarely quantify the policy-driven decoupling effort. To address these gaps, this study employs the generalized Divisia index method to decompose transportation carbon emissions across thirty Chinese provinces from 2005 to 2022. Furthermore, we innovatively integrate the Tapio decoupling model with a novel decoupling effort model to assess both the decoupling state and the effectiveness of emission reduction policies. Our key findings reveal that: (1) economic output scale was the primary driver of emission growth, while output carbon intensity was the dominant mitigation factor; (2) driving mechanisms varied considerably across provinces, with 83% of provinces primarily driven by economic scale expansion; (3) the national decoupling state improved from weak to strong decoupling, with 53% of provinces achieving decoupling advancement; and (4) intensity effects were the core driver enabling decoupling efforts, while scale effects represented the primary inhibiting factor. This study provides a robust analytical framework and empirical evidence for formulating differentiated decarbonization strategies across Chinese provinces. Full article

The spatial–temporal pattern, influencing factors and driving variables of carbon emissions are essential considerations for achieving China’s carbon peak and neutrality targets, which support high-quality development. This study was designed to explore and evaluate the spatial–temporal evolutionary characteristics, trends and main influencing factors of carbon emissions in the Yangtze River Economic Belt (YREB), focusing on the decoupling of carbon emissions and socioeconomic development in the YREB. In total, 11 provinces and key cities were focused on as the research objects of the YREB district Tapio decoupling model, which examined the decoupling relationship between carbon emissions and socioeconomic development. Combined with a geographic detector, the Tapio, Logarithmic Mean Divisia Index (LMDI) and gray prediction models were employed in a comprehensive evaluating pipeline, which was constructed to decouple the main influencing factors and corresponding impacts of carbon emissions. Particularly, the gray prediction model was employed to predict the carbon emission differences in the YREB sub-regions in 2030. The results indicated the following: (1) The total carbon emissions showed a periodic fluctuation and upward trend with obvious spatial differences, and energy consumption was mainly dominated by coal. (2) The center of carbon emissions was located in Hubei Province in the middle reaches of the Yangtze River, with a standard deviation ellipse showing a “Southwest–Northeast” trend, and most provinces were concentrated in the L-H (low-high) cluster. (3) The entire YREB had achieved carbon emissions decoupling, but it was mainly in a weak decoupling state. (4) Carbon emissions were significantly affected by the indicator E for economic growth, with the indicators EI for energy consumption and I for the added ratio of GDP also bringing greater impacts on carbon reduction contributions. The carbon emission prediction results indicated that the upper and middle reaches of the YREB were more likely to achieve carbon neutrality. Full article

Harmonizing economic growth and carbon emissions is key to reaching the “dual carbon” targets. This research centers on the seven key urban agglomerations within the Yellow River Basin (YRB) and establishes an integrated research framework of decoupling effect quantification–spatial association recognition–driving factor analysis. By combining the Tapio decoupling model, a modified gravity model, social network analysis (SNA), and the Logarithmic Mean Divisia Index (LMDI) method, the study systematically evaluates the decoupling states, spatial association structure, and driving mechanisms between regional carbon emissions and economic growth from 2001 to 2020. The results show that: (1) All seven urban agglomerations exhibit a simultaneous upward trend in both carbon emissions and GDP, but significant regional disparities exist, with some agglomerations demonstrating a green growth pattern where economic growth outpaces carbon emissions. (2) Weak decoupling is the predominant type among urban agglomerations and their constituent cities in the YRB. Notably, some regions have regressed to growing connection or growing negative decoupling during 2016–2020. (3) The spatial network of carbon emission decoupling effects exhibits a core-periphery structure characterized by stronger eastern regions and weaker western regions, with the Shandong Peninsula and Guanzhong Plain urban agglomerations serving as core nodes for regional linkage. (4) Per capita GDP and technological level play a dominant role in promoting decoupling, while energy intensity and the population carrying intensity of the real economy are the primary inhibiting factors; the impact of industrial structure shows an unstable direction. Grounded in these findings, this study formulates differentiated carbon reduction pathways tailored to regional heterogeneity, providing theoretical insights and actionable guidance to facilitate the low-carbon transition and coordinated governance of urban agglomerations. Full article