Search Results (76)

Agriculture contributes 23% of global carbon emissions, with crop cultivation now exceeding livestock as China’s primary emission source, yet how cropping structure evolution drives regional differentiation remains insufficiently quantified. This study examines the Northeast Plain, Huang-Huai-Hai Plain, Middle-Lower Yangtze River Plain, and Xinjiang-Gansu using carbon accounting, LMDI decomposition, and correlation analysis (2000–2021). Cropping structure dominates spatial emission heterogeneity: the Northeast’s shift to corn monoculture (63.6% share) created a carbon hotspot; Huang-Huai-Hai’s wheat–corn rotation (>64%) generated the highest northern intensity; the Yangtze region maintained moderate growth through diversification; and Xinjiang-Gansu showed “scale-intensity dual growth” under arid constraints. Structural effects drove cumulative growth (+1.85 Mt and +1.33 Mt), while regional drivers differed—mechanization scale (r = 0.90), fertilizer dependency (r = 0.84), irrigation–carbon coupling (r = 0.94), and irrigation–fertilizer synergy. The 2015 fertilizer policy marked a turning point in intensity. Differentiated strategies—rotation optimization, input efficiency, water–heat synergy, and water-saving technology—can synergize food security with carbon neutrality. Full article

Water-scarce river basins face the dual challenge of sustaining development progress while maintaining water resources carrying capacity (WRCC), yet city-scale evidence remains limited on how New Quality Productive Force (NQPF)-driven high-quality development reshapes WRCC through coupled coordination and development–pressure decoupling processes. Using a balanced panel of 15 cities in the Weihe River Basin (WRB) during 2014–2023, an integrated analytical framework was implemented by combining composite index evaluation (WRCC and the high-quality development index (HQDI)), the Coupling Coordination Degree (CCD) model, Tapio decoupling diagnosis between HQDI and total water use (TWU), and logarithmic mean Divisia index (LMDI) decomposition. The results indicate that: (1) both the HQD index and WRCC exhibited sustained growth, with their CCD improving significantly from mild imbalance to primary coordination, while a distinct spatial pattern of “Guanzhong leading, northern Shaanxi improving, and eastern Gansu stabilizing” emerged; (2) the HQDI–WRCC linkage was further supported by pooled statistical tests and a two-way fixed effects specification with city-clustered robust standard errors, confirming a significant positive association (Pearson = 0.517, p < 0.01; Spearman = 0.183, p < 0.05) and a stable positive effect of HQDI on WRCC (β = 0.194, p = 0.0088); (3) Tapio results reveal an overall transition from earlier volatility toward a later-period regime dominated by Weak Decoupling (WD) and Strong Decoupling (SD), implying that development progress became less dependent on rising TWU, although pronounced inter-city heterogeneity persisted; (4) LMDI decomposition further identified water use intensity and industrial structure as primary inhibitors of water consumption, whereas the R&D scale effect increased nearly 60-fold, emerging as a major driver of water demand. This study provides a mechanistic basis for coordinating ecological protection and high-quality development under rigid water constraints in water-scarce basins. Full article

Resource-based cities generally have large carbon-emission, and their carbon balance status is receiving more attention. Land use is a key factor in regulating regional carbon balance. To explore the relationship between land use patterns and carbon balance in resource-based cities, we selected nine cities in Anhui, a major energy province, as the research object. Based on the land use data (2000–2020) and the carbon emission coefficient method, we calculated the carbon emissions, carbon sequestration, and net carbon emissions to show their spatiotemporal evolution. The Logarithmic Mean Divisia Index (LMDI) method was employed to explore the driving factors of carbon emissions. The results indicated the following: (1) Net carbon emissions increased by 149.60%, and the growth rate had slowed down since 2015. Forestland constituted the primary carbon sink, whereas cropland was the dominant carbon source. The spatial distribution of carbon emissions and carbon sequestration was uneven. (2) The economic development level and energy consumption density were the principal factors of emission increases. Conversely, carbon emission intensity and land use economic efficiency served as the key mitigating factors. Full article

Addressing land use carbon emissions (LUCE) is critical for mitigating climate change. Using multi-source heterogeneous data from 2010 to 2020, with Land use transition matrix and Kaya-LMDI model, this paper analyzes the spatiotemporal evolution and driving factors of LUCE on Hainan Island. The results indicate the following: (1) The study period witnessed significant land use transitions relevant to carbon stocks. Forest area (a key carbon sink) decreased substantially by 2188.74 km², while construction land (a major emission source) expanded by 182.10 km². (2) Consequently, total net LUCE increased by 54% over the decade. This growth was overwhelmingly driven by a 60.8% increase in carbon emissions from the expansion of construction land. (3) The driver analysis indicates that LUCE growth was significantly promoted by land finance dependence and economic development, with these effects exhibiting significant spatial heterogeneity. This study provides a scientific basis for optimizing low-carbon land use policies and offers critical insights for sustainable development in island areas. Full article

The Yellow River Basin (YRB) is a core region for agricultural production in China; however, its agricultural energy consumption exhibits significant spatial–temporal differences, and it is confronted with the practical demand for the coordination of low-carbon transition and food security. Investigating the driving factors of agricultural energy consumption in the YRB is crucial for optimizing its agricultural energy structure, advancing low-carbon agricultural development, and offering targeted support for regional agricultural sustainability. Based on the data of YRB from 2000 to 2021, this paper employs the Logarithmic Mean Divisia Index (LMDI) method to decompose the driving factors of agricultural energy consumption in the basin by examining the interrelationships among five key factors: water, energy, food, land, and population. The results showed the following: (1) Per capita food production efficiency effect is the main factor driving the increase in agricultural energy consumption, followed by the water consumption output efficiency effect, the effective irrigation rate effect, the actual irrigation ratio effect, and the population scale effect. (2) The agricultural employment structure effect, the energy consumption output efficiency effect, the intensity of agricultural acreage effect, and the irrigation quota effect have reduced agricultural energy consumption. (3) Specifically, in Inner Mongolia, Shanxi and Henan, the largest incremental effect is the per capita food production efficiency effect. However, the primary driver in the remaining six provinces is the water consumption output efficiency effect. Regarding the reduction effect, the largest driver in Gansu, Shanxi and Shandong is the energy consumption output efficiency effect. Further, this paper analyzes the drivers of spatial differences in agricultural energy consumption in nine places. The research results can provide theoretical support and practical references for formulating targeted regional policies for the low-carbon transition of agricultural energy in the YRB. 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

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

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

The arid and semi-arid regions of Northwest China, as major agricultural production zones, have long faced dual challenges: increasing water resource pressure and severe supply–demand imbalances caused by the expansion of cultivated land. The crop water footprint, an effective indicator for quantifying agricultural water use, plays a crucial role in supporting sustainable development in the region. This study adopted a multi-scale spatiotemporal analysis framework, combining the CROPWAT model with Geographic Information System (GIS) techniques to investigate the spatiotemporal evolution of crop water footprints in Northwest China from 2000 to 2020. The Logarithmic Mean Divisia Index (LMDI) model was used to analyze spatial variations in the driving forces. A multidimensional evaluation system—encompassing structural, economic, ecological, and sustainability dimensions—was established to comprehensively assess agricultural water resource utilization in the region. Results indicated that the crop water footprint in Northwest China followed a “decline-increase-decline” trend, it increased from 90.97 billion m³ in 2000 to a peak of 133.49 billion m³ in 2017, before declining to 129.30 billion m³ in 2020. The center of the crop water footprint gradually shifted northward—from northern Qinghai to southern Inner Mongolia—mainly due to rapid farmland expansion and increasing water consumption in northern areas. Policy and institutional effect, together with economic development effect, were identified as the primary drivers, contributing 49% in total. Although reliance on blue water has decreased, the region continues to experience moderate water pressure, with sustainable use achieved in only half of the study years. Water scarcity remains a pressing concern. This study offers a theoretical basis and policy recommendations to enhance water use efficiency, develop effective management strategies, and promote sustainable water resource utilization in Northwest China. Full article

The “Grain for Green” policy has led to a reduction in cultivated land area in the Loess Plateau, intensifying the conflict between ecological conservation and food security. As a key strategy to mitigate this tension, irrigated farmland has undergone significant changes in both its spatial extent and water consumption, which may further exacerbate the water crisis. Hence, the spatio-temporal dynamics and driving forces behind these changes require greater attention and have not yet been comprehensively explored. This study integrates multi-source datasets and employs piecewise linear regression and the Logarithmic Mean Divisia Index (LMDI) model to analyze the spatio-temporal evolution of cultivated land and irrigation water use. Furthermore, it quantifies the contributions of key factors such as cultivated land area, irrigation intensity, and crop planting structure to irrigation water dynamics. The results show that (1) The total cultivated land area in the Loess Plateau decreased by 12.4% from 1985 to 2020, with increases primarily concentrated along the Yellow River between Hekou and Longmen, while decreases were predominantly observed around major cities such as Xi’an, Taiyuan, and Yuncheng. Conversely, the irrigated area exhibited an overall upward trend, with minor declines occurring between 1977 and 1985. (2) While the total irrigation water use increased overall, piecewise linear regression analysis identified four distinct phases, with the first three phases showing growth, followed by a decline after 2001. (3) The expansion of agricultural irrigation areas emerged as the primary driver of increased irrigation water use, whereas advancements in irrigation efficiency effectively reduced water consumption. This study provides novel insights into the spatio-temporal dynamics of irrigation water use in the Loess Plateau and offers valuable guidance for optimizing water resource management and advancing sustainable development in the region. Full article

With rapid urbanization and industrialization, steel in-use stocks (SIUS) have experienced significant growth, playing an important role in urban mining and future renewable resources. Although previous studies have quantified SIUS at the provincial level, a comprehensive understanding of its spatial distribution remains limited. This study uses Henan Province as a case to assess SIUS and its spatial distribution at the provincial level. A spatio-temporal characterization framework is developed to systematically analyze SIUS dynamics, integrating the bottom-up model, the spatial autocorrelation model, the Tapio–LMDI model, and the stock-driven model. The findings show that total SIUS has been continuously increasing, reaching 499.35 Mt in 2023, with the buildings sector being the largest contributor, accounting for 67%. However, due to its large population, per capita SIUS was 5.09 t/cap in 2023, lower than that of China. Spatial analysis reveals significant autocorrelation in per capita SIUS, with notable spatial heterogeneity in its density. Moreover, the average annual growth rate of SIUS is projected to decline from 10% in 2023 to 5% in 2060, suggesting that SIUS in Henan is approaching a saturation phase, consistent with theoretical expectations. Full article

Amidst the backdrop of rural population decline and the inefficient expansion of construction land, traditional land management models are confronted with the dual challenges of supply–demand mismatch and low efficiency. This paper constructs a three-dimensional analytical framework based on decoupling types, development potential, and driving mechanisms. Initially, using Tapio’s decoupling theory, the study identifies the population–land decoupling types among 224 villages in Yanggao County, Shanxi Province, Northern China. It then evaluates the development potential of rural construction land using a comprehensive index system, and a linkage analysis between the two is conducted. Finally, the study employs the Logarithmic Mean Divisia Index (LMDI) method to conduct an in-depth analysis of the mechanisms driving changes in rural construction land. The results indicate the following: (1) Between 2010 and 2020, the study area exhibited a reverse evolution characterized by rural population loss and the expansion of construction land, with a significant “population–land decoupling” phenomenon. (2) The development potential for rural construction land shows a pattern of being high in the north and south, low in the middle, high in mountainous and hilly areas, low in plains, and high in peripheral areas but low in town centers. (3) Villages in Yanggao County are predominantly of the resource reserve type (49.11%), indicating relatively abundant land resource reserves. (4) In different population migration patterns, the reduction in land-use efficiency represents similar proportions (38% and 36%), with villages experiencing net population inflow performing better in improving land-use efficiency compared to those with net population outflow. Drawing on international governance experiences, the study proposes classification and phased implementation pathways. By revealing the dynamic patterns of rural population–land relationships—construction land potential and change mechanisms—the logic of regulatory path adaptation, the article provides a methodological paradigm for constructing a precise and differentiated land resource allocation system, promoting the transition of rural spatial governance from expansion in scale to an improvement in quality. Full article

Life cycle carbon emissions from the construction industry (CE) have a profound impact on China’s “dual carbon” goals, with significant emissions posing severe challenges to the environment. In this paper, four prediction models were trained and compared, and the optimal model, the Genetic Algorithm Optimized BP Neural Network (GA-BP), was finally selected for multi-scenario prediction of CE. Firstly, this study performs a comprehensive accounting and indicator analysis of CE over its entire life cycle. In addition, this paper further conducts a spatial differentiation analysis of CE. Subsequently, parameter analysis was conducted using an improved STIRPAT model, followed by LMDI factor decomposition based on this model. Finally, the model performance was verified using three evaluation metrics: the coefficient of determination (R²), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results indicate that (1) in the carbon emission impact assessment, CE reached a peak of 42.52 t per capita annually and 8.90 t CO₂/m² per unit area; (2) the year-end resident population has the greatest influence on CE, with other related variables also contributing positively; and (3) the GA-BP model outperforms other models, with R² increasing from 0.0435 to 0.0981, MAE reducing from 63% to 76%, and MAPE decreasing from 23% to 68%. Full article