Search Results (154)

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

Achieving decoupling between tourism economic growth and tourism carbon emissions is of paramount importance. This study innovatively integrates the geographically weighted regression (GWR) model—a tool for analyzing spatial heterogeneity—into the Tapio decoupling framework to examine the dynamic decoupling relationship between tourism growth and carbon emissions. It further investigates the driving factors behind decoupling evolution, their interactions, and precisely characterizes the mechanisms, directions, pathways, and intensities of these drivers. Key findings reveal an M-shaped fluctuation trend in tourism carbon emissions within the study area, with significant variations in emission shares across different tourism sectors and transportation modes. Spatially, carbon emissions exhibit heterogeneity and negative autocorrelation, where inter-regional disparities diminish while intra-regional disparities intensify. The tourism economic system in the Yangtze River Economic Belt (YREB) transitioned through weak decoupling, expansive negative decoupling, and strong decoupling states, eventually stabilizing at weak decoupling. Regional decoupling states varied markedly, suggesting that some areas require exploration of new low-carbon development paradigms. For sustainable tourism development, policy-makers should prioritize the decoupling relationship between tourism emissions and economic growth. Region-specific policies must be formulated to facilitate low-carbon transitions, promote industrial upgrading, and enhance inter-regional collaboration—ultimately advancing sustainable tourism under carbon neutrality goals. Full article

Against the backdrop of the deepening implementation of the “Double Carbon” goals, reducing carbon emissions poses great pressure on China. As major agricultural and industrial provinces, the industrial structure of the three northeastern provinces has a crucial impact on carbon emissions. In order to explore this phenomenon, this study employed provincial and municipal data from 2007 to 2019 to simulate the spatial and temporal patterns of carbon emissions and GDP in Northeast China. The Tapio decoupling model was applied to assess the elasticity coefficient between economic development and carbon emissions, while the Theil index was used to evaluate the rationalization of the industrial structure. Then, a multiple linear regression model (MLR) was innovatively applied to explore the relationship between the indexes of the two models. This study found that carbon emissions and GDP in the three provinces both exhibited the characteristic of Liaoning > Heilongjiang > Jilin. In the decoupling analysis, 64.7% of the cities were dominated by benign decoupling. The negative decoupling areas were primarily composed of industrial cities in the southwest and resource-based cities in the east. In the rationalization analysis, there were large-scale irrational areas in 2019, which were concentrated in northwestern and southwestern industrial cities, and occasionally in eastern resource-based cities. There was a certain degree of spatial overlap between these two problematic areas. The MLR result showed that there was a positive correlation between the elasticity coefficient and the Theil index, indicating that optimizing the industrial structure can promote the upgrading of the decoupling status toward strong decoupling. This study provided a theoretical basis for improving the decoupling of carbon emissions and economic development through industrial structure rationalization. For overlapping regions, emission reduction can be prioritized through the rationalization of the industrial structure to achieve a better decoupling status. Full article

To evaluate water pollution status and sustainable development potential in the Huai River Basin, this study focused on the spatiotemporal evolution and influencing factors of the grey water footprint (GWF) across 35 cities in the basin from 2005 to 2020. This study quantifies the GWF from agricultural, industrial, and domestic perspectives and analyzes its spatial disparities by incorporating spatial autocorrelation analysis. The Tapio decoupling model was applied to explore the relationship between pollution and economic growth, and geographic detectors along with the STIRPAT model were utilized to identify driving factors. The results revealed no significant global spatial clustering of GWF in the basin, but a pattern of “high in the east and west, low in the north and south” emerged, with high-value areas concentrated in southern Henan and northern Jiangsu. By 2020, 85.7% of cities achieved strong decoupling, indicating improved coordination between the environment and economy. Key driving factors included primary industry output, crop sown area, and grey water footprint intensity, with a notable interaction between agricultural output and grey water footprint intensity. The quantitative analysis based on the STIRPAT model demonstrated that seven factors, including grey water footprint intensity and total crop sown area, exhibited significant contributions to influencing variations. Ranked by importance, these factors were grey water footprint intensity > total crop sown area > urbanization rate > population size > secondary industry output > primary industry output > industrial wastewater discharge, collectively explaining 90.2% of the variability in GWF. The study provides a robust scientific basis for water pollution control and differentiated management in the river basin and holds significant importance for promoting sustainable development of the basin. Full article

Our contemporary society is powered by fossil fuels, which results in environmental catastrophes. The combustion of these materials results in the release of CO₂, which accelerates the progression of climate change and its catastrophic consequences. The environmental repercussions of fossil fuel extraction have been highlighted through research into alternative energy sources. This inquiry uses the Tapio-Z decoupling approach to assess energy inputs and emissions. Furthermore, the fuzzy logic model is used to inspect the economic growth of the USA and China, as well as the impact of environmental factors, energy sources, and utilization, through decoupling effects from 1994 to 2023. The findings are substantiated by the individual perspectives of the environmental factors regarding decoupling, which ultimately lead to the acquisition of valuable results. We anticipate a substantial reduction in the total volume of CO₂ emissions in both the USA and China. Compared to China, the USA shows a significant increase in CO₂ emissions due to its reliance on fossil fuels. It is evident that a comprehensive transition to renewable resources and a broad range of technology is required to mitigate CO₂ emissions in high-energy zones. In their pursuit of sustainability, these two nations are making remarkable strides. The percentage change in CO₂ emissions indicates that effective changes in economic growth, energy input, and energy utilization, particularly sustainable energy, transmute energy output, as does the sustained implementation of robust environmental protection policies. The percentage change in CO₂ emissions indicates a remarkable transformation in energy input, energy consumption, and economic growth. This transition has been most visible in the areas of energy transformation, sustainability, and the maintenance of strong environmental protection measures. Full article

To support carbon peaking and neutrality goals in the city transportation sector, this paper accounts for CO₂ emissions from the transport sector in Xuzhou City, North Jiangsu Province, from 1995 to 2023. This study explores the relationship between transport-related carbon emissions and economic growth, using the TAPIO decoupling index. Meanwhile, a carbon emission prediction model based on the STIRPAT framework is constructed, with scenario analysis applied to forecast future emissions. Results show three decoupling stages: the first, dominated by weak and expansive negative decoupling, reflects extensive economic growth; the second features weak decoupling with expansive coupling, indicating a more environmentally coordinated phase; the third transitions from expansive negative decoupling and weak decoupling to strong decoupling and expansive coupling, suggesting a shift in development patterns. Under the baseline, low-carbon, and enhanced low-carbon scenarios, by 2030, the CO₂ emissions of the transportation industry in Xuzhou will be 10,154,700 tons, 9,072,500 tons, and 8,835,000 tons, respectively, and the CO₂ emissions under the low-carbon scenario and the enhanced low-carbon scenario will be reduced by 10.66% and 13.00%, respectively. Based on these findings, the study proposes carbon reduction strategies for Xuzhou’s transport sector, focusing on policy regulation, energy use, and structural adjustments. Full article

Carbon emissions from freshwater aquaculture can exacerbate the greenhouse effect, thereby impacting human life and health. Consequently, it is of great significance to explore the carbon peak process and the role of emission reduction data in China’s freshwater aquaculture industry. This study innovatively employs the Logarithmic Mean Divisia Index model (LMDI) and the Tapio decoupling model to conduct an in-depth analysis of the relationship between carbon emissions and output values in the freshwater aquaculture industry, accurately identifying the main driving factors. Meanwhile, the global and local Moran’s I indices are introduced to analyze its spatial correlation from a new perspective. The results indicate that from 2013 to 2023, carbon emissions from China’s freshwater aquaculture industry exhibited a quasi-“N”-shaped trend, reaching a peak of 38 million tons in 2015. East China was the primary contributor to carbon emissions, accounting for 46%, while South China, Central China, and Northeast China each had an average annual share of around 14%, with Southwest, North China, and Northwest China contributing relatively small proportions. The global Moran’s I index showed a decreasing trend, with a p-value ≤ 0.0010 and a z-score > 3.3, indicating a 99% significant spatial correlation. High-high clusters were concentrated in some provinces of East China, while low-low clusters were found in Northwest, North, and Southwest China. The level of fishery economic development positively drove carbon emissions, whereas freshwater aquaculture production efficiency, industrial structure, and the scale of the aquaculture population had negative effects on carbon emissions. During the study period, carbon emissions exhibited three states: weak decoupling, strong decoupling, and expansive negative decoupling, with alternating strong and weak decoupling occurring after 2015. Full article

Achieving carbon emission reductions in the residential building sector while maintaining economic growth represents a global challenge, particularly in rapidly developing regions with internal disparities. This study examines Jiangsu Province in eastern China—a economic hub with north-south development gradients—to develop an integrated framework for differentiated carbon reduction pathways. The methodology combines spatial autocorrelation analysis, logarithmic mean Divisia index (LMDI) decomposition, system dynamics modeling, and Tapio decoupling analysis to examine urban residential building emissions across three regions from 2016–2022. Results reveal significant spatial clustering of emissions (Moran’s I peaking at 0.735), with energy consumption per unit area as the dominant driver across all regions (contributing 147.61%, 131.60%, and 147.51% respectively). Scenario analysis demonstrates that energy efficiency policies can reduce emissions by 10.1% while maintaining 99.2% of economic performance, enabling carbon peak achievement by 2030. However, less developed northern regions emerge as binding constraints, requiring technology investments. Decoupling analysis identifies region-specific optimal pathways: conventional development for advanced regions, balanced approaches for transitional areas, and subsidies for lagging regions. These findings challenge assumptions about environment-economy trade-offs and provide a replicable framework for designing differentiated climate policies in heterogeneous territories, offering insights for similar regions worldwide navigating the transition to sustainable development. Full article

Understanding the spatio-temporal evolution of farmland carbon emissions, disentangling their underlying driving forces, and exploring the decoupling relationship between these emissions and economic development are pivotal to advancing low-carbon and high-quality agricultural development in Shandong Province, China. Using the Logarithmic Mean Divisia Index (LMDI) and Tapio decoupling model, this study conducted a comprehensive analysis of panel data from 16 cities in Shandong Province spanning 2004–2023. This research reveals that the total farmland carbon emissions in Shandong Province followed a trajectory of “initial fluctuating increase and subsequent steady decline” during the study period. The emissions peaked at 29.4 million tons in 2007 and then declined to 20.2 million tons in 2023, representing a 26.0% reduction compared to the 2004 level. Farmland carbon emission intensity in Shandong Province showed an overall downward trend over the period 2004–2023, with the 2023 intensity registering a 68.9% decline compared to 2004. The carbon emission intensity, agricultural structure, and labor effects acted as inhibiting factors on farmland carbon emissions in Shandong Province, while the economic development effect exerted a positive driving impact on the growth of such emissions. Over the 20-year period, these four factors cumulatively contributed to a reduction of 2.1 × 10⁵ tons in farmland carbon emissions. During 2004–2013, the farmland carbon emissions in Zaozhuang, Yantai, Jining, Linyi, Dezhou, Liaocheng, and Heze showed a weak decoupling state, while in 2014–2023, the farmland carbon emissions and economic development in all cities of Shandong Province showed a strong decoupling state. In the future, it is feasible to reduce farmland carbon emissions in Shandong Province by improving agricultural resource utilization efficiency through technological progress, adopting advanced low-carbon technologies, and promoting the transformation of agricultural industrial structures towards “high-value and low-carbon” designs. Full article