Search Results (57)

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 mining industry (MI) in mineral-rich regions is pivotal for economic growth but is challenged by significant pollution and emissions. This study examines Guangxi, a representative region in China, in light of the country’s “Dual Carbon” goals. We quantified carbon emissions from the MI from 2005 to 2021, employing the generalized Divisia index method (GDIM) to analyze the factors driving these emissions. Additionally, a system dynamics (SD) model was developed, integrating economic, demographic, energy, environmental, and policy variables to assess decarbonization strategies and the potential for carbon decoupling. The key findings include the following: (1) Carbon accounting analysis reveals a rising emission trend in Guangxi’s MI, predominantly driven by electricity consumption, with the non-ferrous metal mining sector contributing the largest share of total emissions. (2) The primary drivers of carbon emissions were identified as economic scale, population intensity, and energy intensity, with periodic fluctuations in sector-specific drivers necessitating coordinated policy adjustments. (3) Scenario analysis showed that the Emission Reduction Scenario (ERS) is the only approach that achieves a carbon peak before 2030, indicating that it is the most effective decarbonization pathway. (4) Between 2022 and 2035, carbon decoupling from total output value is projected to improve under both the Energy-Saving Scenario (ESS) and ERS, achieving strong decoupling, while the resource extraction shows limited decoupling effects often displaying an expansionary connection. This study aims to enhance the understanding and promote the advancement of green and low-carbon development within the MI in mineral-rich regions. Full article

Addressing the sustainability challenges posed by the expanding livestock sector is crucial for China’s green transition. With the transformation of national dietary structure and increasing demand for livestock products, the associated resource consumption and environmental impacts, particularly carbon emissions have intensified. Reducing carbon emissions from livestock is vital for mitigating global warming, enhancing resource utilization efficiency, improving ecosystems and biodiversity, and ultimately achieving sustainable development of the livestock industry. Against this backdrop, this study measures the carbon emissions from livestock sector employing the Life Cycle Assessment (LCA) method, and applies the Generalized Divisia Index Method (GDIM) to analyze the factors affecting the changes in carbon emissions, aiming to quantify and analyze the carbon footprint of China’s livestock sector to inform sustainable practices. The findings reveal that China’s total carbon emissions from the livestock sector fluctuated between 645.15 million tons and 812.99 million tons from 2000 to 2023. Since 2020, emissions have entered a new phase of continuous growth, with a 5.40% increase in 2023 compared to 2020. Significantly, a positive trend toward sustainability is observed in the substantial decline of carbon emission intensity over the study period, with notable reductions in emission intensity across provinces and a gradual convergence in inter-provincial disparities. Understanding the drivers is key for effective mitigation. The output level and total mechanical power consumption level emerged as primary positive drivers of carbon emissions, while output carbon intensity and mechanical power consumption carbon intensity served as major negative drivers. Moving forward, to foster a sustainable and low-carbon livestock sector, China’s livestock sector development should prioritize coordinated carbon reduction across the entire industrial chain, adjust the industrial structure, and enhance the utilization efficiency of advanced low-carbon agricultural machinery while introducing such equipment. Full article

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

To achieve carbon neutrality targets in the power sector, regions with rich coal and renewable energy resources are facing unprecedented pressure. This paper explores the decarbonization pathway in the power sector in Inner Mongolia, China, under different energy transition scenarios based on the Long-Range Energy Alternatives Planning System (LEAP) model. This includes renewable energy expansion, carbon capture and storage (CCS) applications, demand response, and economic regulation scenarios. Subsequently, a combination of the Logarithmic Mean Divisia Index (LMDI) and Slack-Based Measure Data Envelopment Analysis (SBM-DEA) model was developed to investigate the influencing factors and power generation efficiency in low-carbon electricity. The results revealed that this region emphasizes first developing renewable energy and improving the carbon and green electricity market and then accelerating CCS technology. Its carbon emissions are among the lowest, at about 77.29 million tons, but the cost could reach CNY 229.8 billion in 2060. We also found that the influencing factors of carbon productivity, low-carbon electricity structures, and carbon emissions significantly affected low-carbon electricity generation; their cumulative contribution rate is 367–588%, 155–399%, and −189–−737%, respectively. Regarding low-carbon electricity efficiency, the demand response scenario is the lowest at about 0.71; other scenarios show similar efficiency values. This value could be improved by optimizing the energy consumption structure and the installed capacity configuration. Full article

Spanning China’s eastern, central, and western regions, the Yangtze River Economic Belt (YREB) is a pivotal area for economic growth and carbon emissions, with its three major urban agglomerations serving as key hubs along the upper, middle, and lower reaches of the Yangtze River. Understanding the driving factors of carbon emissions and simulating carbon peak scenarios in these regions are critical for informing low-carbon development strategies across China’s diverse geographical zones. This study employs Grey Relational Analysis to identify key drivers and applies the Logarithmic Mean Divisia Index (LMDI) decomposition method to quantify the contributions of various factors to carbon emissions from 2005 to 2021. Furthermore, the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model is utilized to project future emission trends under multiple scenarios. The results indicate that (1) the growth rate of carbon emissions in the three urban agglomerations has generally decelerated during the study period; (2) the influence of driving factors varies significantly across regions, with economic development, urbanization, and population size positively correlating with carbon emissions, while energy structure and energy intensity exhibit mitigating effects; and (3) tailored emission reduction strategies for each urban agglomeration—namely, the Yangtze River Delta Urban Agglomeration (YRD), the Middle Reaches of the Yangtze River Urban Agglomeration (TCC), and the Chengdu-Chongqing Urban Agglomeration (CCA)—can enable all three to achieve carbon peaking by 2030. These findings provide a robust foundation for region-specific policy-making to support China’s carbon neutrality goals. Full article

Chemical fertilizer inputs in China peaked in 2015; however, agricultural carbon emissions continue to rise, and the effect of chemical fertilizer inputs on agricultural carbon emissions remains unclear in this context. This paper aims to offer a useful policy reference for low-carbon agriculture based on agrochemical inputs. Taking northeast China as an example, we incorporated chemical fertilizers as a factor in the generalized Divisia index model (GDIM) and conducted a decoupling analysis using a decoupling effort index (DEI) on data from 2000 to 2020. The factor decomposition results indicate that the chemical fertilizer input scale served as a driving factor with a declining trend, and carbon productivity from chemical fertilizer shifted from an inhibiting effect to a driving effect on agricultural carbon emissions. The results of integrating the GDIM with a DEI indicate that reducing chemical fertilizer inputs and exerting the inhibiting effect of carbon productivity from chemical fertilizer both contribute to effective decoupling. Full article

In recent years, global environmental issues have become increasingly prominent. The transportation industry, as the fundamental sector of national economic development, is also characterized by high energy consumption and carbon emissions. Therefore, it is imperative to conduct research on the carbon emission problem within this industry. In light of the Tapio decoupling model, an analysis of the correlation between traffic carbon emissions and economic development in Guangdong province during 1999–2019 was carried out. With the aim of encouraging Guangdong province’s low-carbon transportation development, the factors affecting the transportation industry are analyzed utilizing the generalized Divisia index model (GDIM). We also introduced passenger and freight turnover as an influencing factor for analysis. The findings indicate that (1) Guangdong province’s traffic carbon emissions increased from 1999 to 2019; (2) the traffic carbon emissions’ decoupling effect is mainly “weakly decoupled”, and the overall decoupling effect is not strong in Guangdong province; (3) among the traffic carbon emissions’ factors, the effects of the production value of traffic and the turnover volume are at the forefront, and the effect of turnover volume has gradually exceeded the production value of traffic in recent years. The suppression of the intensity of carbon emissions is relatively large, while the suppression of the intensity of energy consumption and transport is relatively weak. Based on this, strategies were proposed to promote a cleaner energy mix, improve energy use efficiency, create energy savings, develop green technologies, and foster the restructuring of transportation. Full article

Analyzing the spatiotemporal changes and influencing factors of carbon emissions generated by land use is of great importance for improving land use structure and promoting regional low-carbon economic development. This study, based on remote sensing and statistical yearbook data from 1995 to 2020, calculated the carbon emissions from land use in Jiangxi Province, China. Multiple spatial analysis methods and the logarithmic mean Divisia index were used to elucidate the spatiotemporal evolution and driving factors of carbon emissions, and the findings revealed the following: (1) The spatiotemporal changes in land use in Jiangxi Province during 1995–2020 were substantial as forest land accounted for 65% of the entire land area, while construction land increased by 98.1%. Cultivated land decreased the most, followed by forest land. (2) There was a fourfold rise in carbon emissions in Jiangxi Province, driven primarily by construction land, and northern areas produced higher carbon emissions compared with central and southern regions. Forest land was the main carbon sink. (3) Economic development (257.36%) and the impact of the proportion of construction land (211.31%) were the primary factors contributing to the increase in carbon emissions from land use, while other factors had inhibitory effects. This study transformed the macroscale low-carbon development strategy of cities into targeted local policies, and the research theories and methods adopted could provide scientific reference for other regions in urgent need of carbon reduction worldwide. Full article

The Logarithmic Mean Divisia Index (LMDI) method is widely applied in research on carbon emissions, urban energy consumption, and the building sector, and is useful for theoretical research and evaluation. The approach is especially beneficial for combating climate change and encouraging energy transitions. During the method’s development, there are opportunities to develop advanced formulas to improve the accuracy of studies, as indicated by past research, that have yet to be fully explored through experimentation. This study reviews previous research on the LMDI method in the context of building carbon emissions, offering a comprehensive overview of its application. It summarizes the technical foundations, applications, and evaluations of the LMDI method and analyzes the major research trends and common calculation methods used in the past 25 years in the LMDI-related field. Moreover, it reviews the use of the LMDI in the building sector, urban energy, and carbon emissions and discusses other methods, such as the Generalized Divisia Index Method (GDIM), Decision Making Trial and Evaluation Laboratory (DEMATEL), and Interpretive Structural Modeling (ISM) techniques. This study explores and compares the advantages and disadvantages of these methods and their use in the building sector to the LMDI. Finally, this paper concludes by highlighting future possibilities of the LMDI, suggesting how the LMDI can be integrated with other models for more comprehensive analysis. However, in current research, there is still a lack of an extensive study of the driving factors in low-carbon city development. The previous related studies often focused on single factors or specific domains without an interdisciplinary understanding of the interactions between factors. Moreover, traditional decomposition methods, such as the LMDI, face challenges in handling large-scale data and highly depend on data quality. Together with the estimation of kernel density and spatial correlation analysis, the enhanced LMDI method overcomes these drawbacks by offering a more comprehensive review of the drivers of energy usage and carbon emissions. Integrating machine learning and big data technologies can enhance data-processing capabilities and analytical accuracy, offering scientific policy recommendations and practical tools for low-carbon city development. Through particular case studies, this paper indicates the effectiveness of these approaches and proposes measures that include optimizing building design, enhancing energy efficiency, and refining energy-management procedures. These efforts aim to promote smart cities and achieve sustainable development goals. Full article

The escalating concern over global warming has garnered significant international attention, with carbon emission intensity emerging as a crucial barrier to sustainable economic development across various regions. While previous studies have largely focused on annual scales, this study introduces a novel examination of Tianjin’s quarterly carbon emission intensity and its influencing factors from 2012 to 2022 using quarterly data and the Logarithmic Mean Divisia Index (LMDI) model. The analysis considers the carbon emission effects of thermal power generation, the power supply structure, power intensity effects, and economic activity intensity. The results indicate a general decline in Tianjin’s carbon emission intensity from 2012 to 2020, followed by an increase in 2021 and 2022. This trend, exhibiting significant seasonal fluctuations, revealed the highest carbon emission intensity in the first quarter (an average of 1.4093) and the lowest in the second quarter (an average of 1.0019). Economic activity intensity emerged as the predominant factor influencing carbon emission intensity changes, particularly notable in the second quarter (an average of −0.0374). Thermal power generation and electricity intensity effects were significant in specific seasons, while the power supply structure’s impact remained relatively minor yet stable. These findings provide essential insights for formulating targeted carbon reduction strategies, underscoring the need to optimize energy structures, enhance energy efficiency, and account for the seasonal impacts of economic activity patterns on carbon emissions. Full article

Carbon neutrality is urgent as rapidly emerging economies aggravate their share of global energy demand. In China, the energy structure is dominated by fossil fuels, but it varies significantly across provinces. As an indicator of carbon neutrality, previous studies of decoupling between carbon dioxide emissions and economic growth focused at the national and sector levels in China. However, they overlook the role of industrial structure in decoupling at the provincial level. In this light, the following paper focuses on Qinghai Province, analyzing decoupling and its influencing factors for achieving carbon neutrality from an industrial structure perspective over 1990–2021. It uses the Tapio decoupling model to evaluate decoupling states and the Logarithmic Mean Divisia Index decomposition to evaluate the influencing factors. A Data Envelopment Analysis model of super-efficiency Slacks-Based Measure is used to evaluate the decarbonization efficiency. The study finds that the overall trend shifted from weak to strong decoupling. Strong decoupling dominated the primary industry while weak decoupling dominated the secondary and tertiary industries. Economic growth negatively impacted overall decoupling, while population had a marginal effect. Energy structure and intensity generally promoted decoupling. Additionally, the overall mean efficiency of decarbonization was 0.95, led by the tertiary industry. The paper concludes by discussing policy implications. Full article

The energy consumption by industrial enterprises above designated size in China’s coastal region is the main source of CO₂ emissions. This study analyzes the spatial and temporal evolution patterns and driving factors of CO₂ emissions due to the energy consumption by industrial enterprises above designated size. Enterprises in 82 cities in China’s coastal regions were studied from 2005 to 2020 based on their CO₂ emissions and socio-economic data. The Exploring Spatial Data Analysis (ESDA) methodology and Logarithmic mean Divisia Index decomposition (LMDI model) were used. The results show that, during the study period, energy-related CO₂ emissions from industrial enterprises above designated size in China’s coastal areas generally show a fluctuating upward trend. However, a few cities showed a trend from steady growth to a peak and then a slow decline, which may realize the “double carbon” target in advance. The spatial correlation of CO₂ emission intensity showed a decreasing and then increasing trend, and there were spatial aggregation characteristics in some cities. Among the driving factors, the pull effect is higher than the inhibition effect; the output scale contributes the most to the pull effect, and labor productivity contributes the most to the inhibition effect. The results of this study have a certain reference value for the realization of the “double carbon” target in China’s coastal regions. Full article

The EU power sector is under increasing pressure due to rising electricity demand and the need to meet decarbonisation targets. Member states have been active in investing in renewables and building capacity to increase their share of renewables in electricity generation. However, it is important to examine what progress each member state has made in the deployment of renewable energy for electricity generation and what factors influence gross electricity generation from renewable energy. In this study, logarithmic mean Divisia index (LMDI) analysis was used to examine the changes in EU-27 countries’ gross electricity generation from renewable energy sources (RES), wind, and solar PV from 2012 to 2021. The results show that the RES deployment per capita effect and the RES share effect were the main positive factors for the total gross electricity generation from RES in the EU. In contrast, the RES capacity productivity effect and the energy intensity effect had negative contributions. Population growth had a positive influence but was less significant than the other factors. The deployment of RES per capita effect was the main factor in the overall growth of gross electricity generation from RES in Northern Europe, Central Western Europe, and Central Eastern Europe, according to comparisons between the regional groups. RES share effect was the main driver in Southern Europe. The decrease in RES capacity productivity was the second most important factor influencing the variation in the amount of energy generated by RES in Northern Europe and Central Western Europe. The results could be used to develop more effective and tailored renewable energy policies that take into account the existing main drivers of RES, wind, and solar energy in each of the EU-27 member states. Full article

The electric power sector plays a central role in changing the EU’s energy landscape and establishing Europe as the first climate-neutral continent in the world. This paper investigates fundamental shifts in the EU’s electric power sector by carrying out its logarithmic mean Divisia index decomposition by stages of electricity flows on a large-scale basis (for both the entire EU and its 25 member states) for the period 1995–2021 and identifies the individual contribution of each EU member state to these shifts. In this study, four decomposition models were proposed and 14 impact factors (extensive, structural, and intensive) affecting the development of the EU electric power sector were evaluated in absolute and relative terms. It was found that the wind–gas transition, which took place in the EU’s electric power sector, was accompanied by an increase in the transformation efficiency of inputs in electricity generation and a drop in the intensity of final energy consumption. The non-industrial reorientation of the EU’s economy also resulted in a decrease in the final electricity consumption. At the same time, this transition led to negative shifts in the structure and utilization of its generation capacities. The fundamental shifts occurred mainly at the expense of large economies (Germany, France, Spain, and Italy), but smaller economies (Romania, Poland, Croatia, the Netherlands, and others) made significant efforts to accelerate them, although their contributions on a pan-European scale were less tangible. Full article