Search Results (287)

Cities are key areas for carbon control and reduction. The study of the decoupling between CO₂ emissions and gross domestic product (GDP) and the drivers of CO₂ emissions in cities facilitates the reduction of CO₂ emissions to safeguard the development of the economy. This paper first calculates the CO₂ emissions in Tianjin, China, from 2005 to 2022, then uses the Tapio decoupling index to quantify the decoupling status, and, finally, explores the energy-CO₂-emission drivers through the Logarithmic Mean Divisia Index (LMDI) model. The findings indicate that (1) the decrease in CO₂ emissions from industrial products and transport is the main reason for the decline. (2) During the period under investigation, the predominant condition observed was a state of weak decoupling. (3) Given the economic-output effect is the primary and substantial driver of energy CO₂ emissions, it is essential to harmonize the interplay between economic-development approach and CO₂ emissions to foster sustainable development in Tianjin. The industrial structure plays the most critical role in hindering the reduction of CO₂ emissions; therefore, optimizing industrial structure can help achieve carbon reduction and control targets. These findings enrich the study of CO₂ emission factors and can also interest urban policymakers. Full article

Environmental pollution litigation cases (EPLCs) are experiencing a significant upward trend attributable to the extensive discharge of pollutants in China. However, the driving factors of EPLCs remain ambiguous. Herein, a comprehensive research framework is established by using the logarithmic mean divisia index (LMDI) method for investigating the driving factors of China’s EPLCs. The provinces of Henan, Jilin, Shandong, Zhejiang, Jiangsu, and Guangdong stand out as the regions with the highest number of EPLCs. The GDP per capita and incidence rate promote the incidence of EPLCs, while emission intensity and emission intensity per unit area inhibit the occurrence of EPLCs. Population and population density have less impact on EPLCs. These findings should serve as references for controlling the occurrence of EPLCs in different provinces in China. Full article

Fishery plays an important role in world trade; however, the embodied energy associated with fishery remains incompletely quantified. In this study, we applied the multi-regional input-output (MRIO) model and logarithmic mean Divisia index (LMDI) approach to understand the evolution and drivers of embodied energy in the intermediate and final fishery trade between China and countries along the 21st century Maritime Silk Road (MSR) from 2006 to 2021. The findings are as follows: (1) Embodied energy in the intermediate fishery trade averaged 92.2% of embodied energy from the total fishery trade. China has gradually shifted from being a net exporter to a net importer of embodied energy in intermediate, final, and total fishery trade with countries along the MSR. (2) From a regional perspective, the embodied energy in China’s fishery trade with Japan, South Korea, and Southeast Asia comprises the majority of the embodied energy from China’s total fishery trade (82.0% on average annually). From a sectoral perspective, petroleum, chemical and non-metallic mineral products, and transport equipment were prominent in the embodied energy of China’s intermediate fishery trade (64.0% on average annually). (3) Economic output increases were the main contributors to the increasing embodied energy in all types of fishery trade in China. The improvement in energy efficiency effectively reduced the embodied energy in all types of fishery trade in China, but its negative driving force weakened in recent years owing to minor energy efficiency improvements. Understanding the embodied energy transactions behind the intermediate and final fishery trade with countries along the MSR can provide a theoretical reference for China to optimize its fishery trade strategy and save energy. Full article

The contemporary process of economic development necessitates a heightened focus on matters of sustainability, with a particular emphasis on sustainable energy policy. This is of paramount importance for the protection of the natural environment and the achievement of long-term economic growth. In the context of countries such as the Czech Republic and Poland, which have historically relied on high-carbon energy sources, the transition to a more sustainable energy system represents a significant challenge. The objective of this paper is to undertake a comparative analysis of the trends in energy sustainability in the Czech Republic and Poland from 2017 to 2021, with a particular focus on key performance indicators. The analysis, based on data from the OECD database, revealed notable discrepancies in the rate of change between the two countries, with Poland exhibiting a more pronounced surge in the proportion of renewable energy sources (RES). A Student’s t-test confirmed the existence of statistically significant differences in key indicators between the Czech Republic and Poland, thereby underscoring the diverse challenges that both countries encounter in their pursuit of sustainable energy development. The Granger causality test was employed to ascertain whether variables exhibit temporal relationships that may suggest potential correlations. However, it is important to note that this test does not prove direct causality, but rather indicates that the variables are related at a specific point in time. Interpretation of the results must be undertaken with caution, as the test does not account for the full complexity of relationships between variables, including external factors and structural changes in the economy. Meanwhile, the LMDI decomposition analysis identified the principal drivers of alterations in CO₂ emissions. The findings indicate that, despite advancements in sustainable energy development, Poland and the Czech Republic are confronted with distinctive challenges that necessitate the implementation of tailored policy responses. It is therefore recommended that further investment in renewable energy and the modernisation of energy infrastructure be made in order to achieve long-term sustainability goals. Full article

In light of China’s ambitious goals to peak carbon emissions by 2030 and achieve carbon neutrality by 2060, this study uniquely explores the decoupling dynamics between economic growth and carbon emissions in Xinjiang using panel data from 2006 to 2020 across various prefectures and cities. By employing the Tapio decoupling elasticity index and the Logarithmic Mean Divisia Index (LMDI) decoupling model, we found that Xinjiang, as a whole, has not fully decoupled its carbon emissions from economic growth, with overall emissions below the national average. The carbon emissions growth rate in Xinjiang has significantly decreased from 17.7% during 2005–2010 to 3.35% in 2015–2020, with weak decoupling particularly evident in northern and eastern regions. To achieve full decoupling, it is imperative for policymakers to reform the economic growth model in northern Xinjiang and restructure the energy mix in eastern Xinjiang. Additionally, the promotion of low-carbon industries and the enhancement of green energy efficiency are crucial for advancing the region’s sustainable development. 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

Promoting low-carbon agriculture is vital for climate action and food security. State farms serve as crucial agricultural production bases in China and are essential in reducing China’s carbon emissions and boosting emission efficiency. This study calculates the carbon emissions of state farms across 29 Chinese provinces using the IPCC method from 2010 to 2022. It also evaluates emission efficiency with the Super-Slack-Based Measure (Super-SBM model) and analyzes influencing factors using the Logarithmic Mean Divisia Index (LMDI) method. The findings suggest that the three largest carbon sources are rice planting, chemical fertilizers, and land tillage. Secondly, agricultural carbon emissions in state farms initially surge, stabilize with fluctuations, and ultimately decline, with higher emissions observed in northern and eastern China. Thirdly, the rise of agricultural carbon emission efficiency is driven primarily by technological progress. Lastly, economic development and industry structure promote agricultural carbon emissions, while production efficiency and labor scale reduce them. To reduce carbon emissions from state farms in China and improve agricultural carbon emission efficiency, the following measures can be taken: (1) Improve agricultural production efficiency and reduce carbon emissions in all links; (2) Optimize the agricultural industrial structure and promote the coordinated development of agriculture; (3) Reduce the agricultural labor scale and promote the specialization, professionalization, and high-quality development of agricultural labor; (4) Accelerate agricultural green technology innovation and guide the green transformation of state farms. This study enriches the theoretical foundation of low-carbon agriculture and develops a framework for assessing carbon emissions in Chinese state farms, offering guidance for future research and policy development in sustainable agriculture. Full article

Enhancing urban ecological welfare performance is essential for achieving sustainable urban development and fostering a comprehensive regional green transformation. This study develops a quantitative assessment framework for urban ecological welfare performance, grounded in both the welfare of urban residents and their consumption of ecological resources. Employing the spatio-temporal Logarithmic Mean Divisia Index model to dissect the ecological welfare performance across 108 key prefecture-level cities within China’s Yangtze River Economic Belt, considering both temporal and spatial dimensions, the analysis reveals a “W”-shaped trajectory in the ecological welfare performance from 2006 to 2022, characterized by pronounced spatial disparities. Particularly in the downstream coastal regions and notably the Yangtze River Delta, advantages in social and economic structures, along with public fiscal outlays, contribute to a superior ecological welfare performance, exhibiting a notable spatial spillover effect. The study introduces six key factors—social benefit, economic benefit, population dispersion, population density in urban areas, urbanization scale, and ecological sustainability—to examine their influence on ecological welfare performance, uncovering substantial differences in the outcomes of temporal and spatial decomposition. Temporal decomposition indicates that economic benefit and urbanization scale are the primary drivers enhancing ecological welfare performance, whereas population dispersion is identified as the primary inhibitor. Spatial decomposition reveals that the determinants of above-average urban ecological welfare vary regionally and undergo dynamic shifts over time. Overall, a holistic understanding of the interplay among economic growth, ecological preservation, and the enhancement of residents’ welfare can inform the development and execution of tailored policies by local governments. 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

The standard of living has significantly risen along with ongoing economic progress, but CO₂ emissions have also been rising. The reduction in CO₂ resulting from the daily activities of residents has become a crucial priority for every province. A relevant study on the carbon emissions of Hebei Province residents was conducted for this publication, aiming to provide a theoretical basis for the sustainable development of Hebei Province. The first part of the article calculates the carbon emissions of Hebei Province people from 2005 to 2020 using the emission factor method and the Consumer Lifestyle Approach (CLA). Secondly, the Logarithmic Mean Divisia Index (LMDI) decomposition approach is used to assess the components that influence both direct and indirect carbon emissions. Finally, the scenario analysis approach is employed in conjunction with the LEAP model to establish baseline, low-carbon, and ultra-low-carbon scenarios to predict the trend of residents’ carbon emissions in Hebei Province from 2021 to 2040. The results show that the total carbon emissions of residents in Hebei Province from 2005 to 2020 rose, from 77.45 million tons to 153.35 million tons. Income level, energy consumption intensity, and population scale are factors that contribute to the increase in direct carbon emissions, while consumption tendency factors have a mitigating effect on direct carbon emissions. Economic level, consumption structure, and population scale factors are factors that contribute to the increase in indirect carbon emissions, while energy consumption intensity and energy structure factors have a mitigating effect on indirect carbon emissions. The prediction results show that under the baseline scenario, the cumulative residents’ carbon emissions in Hebei Province will not reach a zenith from 2021 to 2040. However, under the low-carbon situation, the carbon emissions of residents in Hebei Province will peak in 2029, with a peak of 174.69 million tons, whereas under the ultra-low-carbon scenario, it will peak in 2028, with a peak of 173.27 million tons. Full article

The North China Plain (NCP) serves as a critical grain-producing region in China, playing a pivotal role in ensuring the nation’s food security. A comprehensive analysis of the carbon footprint (CF) related to the cultivation of major grain crops within this region and the proposal of strategies to reduce emissions through low-carbon production methods are crucial for advancing sustainable agricultural practices in China. This study employed the lifecycle assessment (LCA) method to estimate the CF of wheat, maize, and rice crops over a period from 2013 to 2022, based on statistical data collected from five key provinces and cities in the NCP: Beijing, Tianjin, Hebei, Shandong, and Henan. Additionally, the Logarithmic Mean Divisia Index (LMDI) model was utilized to analyze the influencing factors. The results indicated that the carbon footprints per unit area (CF_A) of maize, wheat, and rice increased between 2013 and 2022. Rice had the highest carbon footprint per unit yield (CF_Y), averaging 1.1 kg CO₂-eq kg⁻¹, with significant fluctuations over time. In contrast, the CF_Y of wheat and maize remained relatively stable from 2013 to 2022. Fertilizers contributed the most to CF composition, accounting for 48.8%, 48.0%, and 25.9% of the total carbon inputs for wheat, maize, and rice, respectively. The electricity used for irrigation in rice production was 31.8%, which was much higher than that of wheat (6.8%) and maize (7.1%). The LMDI model showed that the labor effect was a common suppressing factor for the carbon emissions of maize, wheat, and rice in the NCP, while the agricultural structure effect and the economic development effect were common driving factors. By improving the efficiency of fertilizer and pesticide utilization, cultivating new varieties, increasing the mechanical operation efficiency, the irrigation efficiency, and policy support, the CF of grain crop production in the NCP can be effectively reduced. These efforts will contribute to the sustainable development of agricultural practices in the NCP and support China’s efforts to achieve its “double carbon” target. Full article

The high carbon emissions and pollution of China’s thermal power industry chain have exacerbated environmental and climate degradation. Therefore, accelerating the green transformation process is of great significance in promoting the sustainable development of enterprises. This study selected 30 listed thermal power enterprises in China as research objects, analyzed their data from 2018 to 2022, set targeted input–output indicators for different stages, and used a two-stage dynamic data envelopment analysis (DEA) model to evaluate and measure the efficiency of the green transformation of Chinese thermal power enterprises. In addition, this study also uses the logarithmic mean Divisia index (LMDI) method to analyze the driving effects of green transformation. The results indicate that in terms of overall efficiency, there is a significant difference in the overall performance of these 30 thermal power enterprises, with a large difference in average efficiency values. Efficiency values are related to enterprise size. In terms of stage efficiency, the average efficiency value of thermal power enterprises in the profit stage was significantly higher than that in the transformation stage, and the profitability of Chinese thermal power enterprises was better. In terms of sub-indicator efficiency, the efficiency of each indicator shows a “U”-shaped trend, and there is a certain correlation between the operating costs and revenue of thermal power enterprises, the market value of green transformation, and related indicators. In addition, the most important factor affecting the efficiency of green transformation is the sewage cost they face, whereas their operational capabilities have the least impact on their green transformation. In this regard, thermal power enterprises should increase their investment in the research and development of key technologies for thermal power transformation and continuously optimize their energy structure. The government will increase financial support for thermal power green transformation enterprises and correspondingly increase emission costs. Full article

The construction industry has become one of the industries that accounts for a relatively large share of China’s total carbon emissions. Aiming at the problems of monitoring difficulties, diversity of segmentation types, and uncertainty of carbon emission factors, this study calculates the carbon emissions and intensity of the construction industry in each province of China from 2010 to 2019, analyzes its spatial and temporal variability using the Moran index and the slope index, analyzes the driving factors by combining the Kaya equation and the LMDI method, and verifies the zero-error characteristics by using the IPAT model. The results show that from 2010 to 2019, carbon emissions from the construction industry in China’s provincial areas increased in general, with a distribution of “high in the east and low in the west”, and the carbon emission intensity declined in general, but some provinces in the north and the center are still higher. Economic development and the increase in housing construction area are the main reasons for the growth of carbon emissions, while the optimization of energy structure and the adjustment of population density reduce carbon emissions. Moreover, the IPAT model verifies the credibility of the results of the LMDI model. This study provides a reference for monitoring and assessing carbon emissions in China’s construction industry from the perspective of spatio-temporal characterization, helps regional energy conservation and emission reduction and dual-carbon strategy, and it analyzes the provincial carbon emission intensity to reveal the low-carbon development issues. Full article

Accurately predicting the trajectory of carbon emissions is vital for achieving a sustainable shift toward a green and low-carbon future. Hence, this paper created a novel model to examine the driver analysis and integrated prediction for Chinese carbon emission, a large carbon-emitting country. The logarithmic mean divisia index (LMDI) approach initially served to decompose the drivers of carbon emissions, analyzing the annual and staged contributions of these factors. Given the non-stationarity and non-linear characteristics in the data sequence of carbon emissions, a decomposition–integration prediction model was proposed. The model employed the empirical mode decomposition (EMD) model to decompose each set of data into a series of components. The various carbon emission components were anticipated using the long short-term memory (LSTM) model based on the deconstructed impacting factors. The aggregate of these predicted components constituted the overall forecast for carbon emissions. The result indicates that the EMD-LSTM model greatly decreased prediction errors over the other comparable models. This paper makes up for the gap in existing research by providing further analysis based on the LMDI method. Additionally, it innovatively incorporates the EMD method into the carbon emission study, and the proposed EMD-LSTM prediction model effectively addresses the volatility characteristics of carbon emissions and demonstrates excellent predictive performance in carbon emission prediction. Full article

Clarifying the factors influencing CO₂ emissions and their peaking pathways in major sectors holds significant practical importance for achieving regional dual-carbon goals. This paper takes Jiangxi, a less developed demonstration zone in central China, as an example. It pioneeringly combines the LMDI method, Tapio decoupling model, and LEAP model to multi-dimensionally analyze the driving mechanisms, evolution patterns, and dynamic relationships with the economic development of carbon emissions in Jiangxi’s key sectors from 2007 to 2021. It also explores the future carbon emission trends and peaking potentials of various sectors under different scenarios. Our results show that (1) Carbon emissions in various sectors in Jiangxi have continued to grow over the past fifteen years, and although some sectors have seen a slowdown in emission growth, most still rely on traditional fossil fuels; (2) Economic growth and industrial structure effects are the main drivers of carbon emission increases, with a general trend towards decoupling achieved across sectors, while agriculture, forestry, animal husbandry and fishery, and ferrous metal smelting have shown a decline in their decoupling status; (3) In the carbon reduction and low-carbon scenarios, the carbon emission peaks in Jiangxi are estimated to be 227.5 Mt and 216.4 Mt, respectively, and targeted strategies for high-emission industries will facilitate a phased peak across sectors and enhance emissions reduction benefits. This has significant reference value for the central region and even globally in formulating differentiated, phased, sector-specific carbon peaking plans, and exploring pathways for high-quality economic development in tandem with ecological civilization construction. Full article