Tracking the change in Spanish greenhouse gas emissions through an LMDI decomposition model: A global and sectoral approach

The reduction of GHG emissions to reverse the greenhouse effect is one of the main challenges in this century. In this paper we pursue two objectives. First, we analyze the evolution of GHG emissions in Spain in 2008-2018, at both the global and sectoral levels, with the variation in emissions decomposed into a set of determining factors. Second, we propose several actions specifically oriented to more tightly controlling the level of emissions. Our results showed a remarkable reduction (18.44%) in GHG emissions, mainly due to the intensity effect, but also to the production-per-capita effect. We detected somewhat different patterns among the various sectors analyzed. While the intensity effect was the most influential one in the agricultural, transport, and others sectors, the production-per-capita effect was predominant in the case of industry. The carbonization effect was revealed as crucial in the commerce sector. The above findings highlight the importance of the energy efficiency measures taken in recent years in the Spanish economy, also pointing to the need to deepen those strategies and to propose new measures that entail greater efficiency in emissions. Additional efforts in areas like innovation, R&D, diffusion of more eco-friendly technologies, and a greater use of greener energies all prove to be essential reduction actions to fight the greenhouse effect.

Impact of asset intensity and other energy-associated CO2 emissions drivers in the Nigerian manufacturing sector: A firm-level decomposition (LMDI) analysis

The study considered the impacts of asset intensity and other energy-associated CO 2 emissions drivers in the Nigerian manufacturing sector from 2010 to 2020. The Logarithmic Mean Divisia Index (LMDI) was used to explore the driving factors of CO 2 emissions: asset intensity, economic output, economic structure, energy intensity, energy mix, and carbon emission coefficient. From the results, the CO 2 emissions decreased from 7.49 MtCO 2 in 2010 to 3.22 MtCO 2 in 2020. Furthermore, among the emissions drivers, the energy mix effect increased CO 2 emissions by 0.50 MtCO 2 , followed by asset intensity (0.29 MtCO 2 ) and economic structure (0.11 MtCO 2 ) . The energy intensity, economic output, and emission coefficient effects inhibited CO 2 emissions by -4.64 MtCO 2 , -0.42 MtCO 2 , and -0.01 MtCO 2 respectively. The contribution of the subsectors' emissions shows that the Other Manufacturing subsector emitted 14.62 MtCO 2 , while Chemical and Pharmaceutical emitted 14.61 MtCO 2 , Food, Beverages and Tobacco, 7.55 MtCO 2 , Textile, Apparel, and Footwear, 6.63 MtCO 2 , Basic Metal and Iron and Steel, 5.15 MtCO 2 , Plastic and Rubber Products, 2.99 MtCO 2 , Agro-Allied, 2.71 MtCO 2 , Oil Refining, 2.01 MtCO 2 , and Pulp and Paper Products, 1.76 MtCO 2 . The results indicated that the effect of asset intensity on emission growth is significant and should not be overlooked. Likewise, the effects of CO 2 emission drivers were found to impact differently across the subsectors. The latter suggests that firm-specific indicators in the respective subsectors should be one of the primacies during policy development since the driving factors of CO 2 emissions fluctuate across the subsectors.

Energy and social factor decomposition to identify drivers impeding sustainable environmental transition in emerging countries: SDGs-2030 progress assessment using LMDI analysis

The balance between human growth, economic prosperity, and the consumption of hydrocarbon energy factors has become a prerequisite for environmental sustainability. However, the complexities of these factors force researchers to work for more viable combinations of such a balance. Therefore, this study attempted to determine the factors driving environmental sustainability in leading populated economies. For this purpose, the Logarithmic Mean Division Index (LMDI) utilized to decompose critical factors such as activity, economy, real density, energy intensity, and suburban effects for the period 1999-2022. Both population and its consequences (economic activity) have been found to be the leading factors behind environmental fluctuations, and energy has a negative impact on hydrocarbon forms, while contributing positively to environmental sustainability with high efficiency and low intensity. Therefore, sustainable demographic and energy transitions can be leading pathways for environmental sustainability in developing economies.

The race between global economic growth and carbon emissions: based on a comparative study of developed and developing countries

In recent years, there has been a persistent intensification of the global greenhouse effect. Balancing carbon emission reduction with economic growth poses an unprecedented global challenge. To better comprehend the relationship between economic growth and carbon emissions, this study first utilized the Tapio decoupling index to compare the decoupling relationship (the USA, Japan, and Germany) and three developing countries (China, India, and Russia) from 2000-2020. Additionally, the logarithmic mean Divisia index (LMDI) method was employed to investigate the factors influencing changes in carbon emissions. Our findings indicate that (1) the USA and Germany basically achieved strong decoupling; China, India, and Russia mainly showed weak decoupling; and Japan showed recessive decoupling. (2) Economic growth predominantly contributed to increased carbon emissions, with a lesser impact from population growth. A significant reduction in energy intensity restrained carbon emissions growth, as did energy structure replacement in most countries, excluding Japan. Based on this, a decoupling effort index was formulated. It has shown that the decoupling efforts made by developing countries are weaker than those of developed countries, primarily attributed to a lesser degree of decoupling between energy intensity and structure. This paper offers valuable insights for developing countries undergoing a low-carbon economic transformation. They should counterbalance carbon emission escalation resulting from economic growth through technological and energy structure improvements.

Spatial-temporal characteristics and driving factors' contribution and evolution of agricultural non-CO2 greenhouse gas emissions in China: 1995-2021

Comprehending the spatial-temporal characteristics, contributions, and evolution of driving factors in agricultural non-CO2 greenhouse gas (GHG) emissions at a macro level is pivotal in pursuing temperature control objectives and achieving China's strategic goals related to carbon peak and carbon neutrality. This study employs the Intergovernmental Panel on Climate Change (IPCC) carbon emissions coefficient method to comprehensively evaluate agricultural non-CO2 GHG emissions at the provincial level. Subsequently, the contributions and spatial-temporal evolution of six driving factors derived from the Kaya identity were quantitatively explored using the Logarithmic Mean Divisia Index (LMDI) and Geographical and Temporal Weighted Regression (GTWR) methods. The results revealed that the distribution of agricultural non-CO2 GHG emissions is shifting from the central provinces to the northwest regions. Moreover, the dominant driving factors of agricultural non-CO2 GHG emissions were primarily economic factor (EDL) with positive impact (cumulative promotion is 2939.61 million metric tons (Mt)), alongside agricultural production efficiency factor (EI) with negative impact (cumulative reduction is 2208.98 Mt). Influence of EDL diminished in the eastern coastal regions but significantly impacted underdeveloped regions such as the northwest and southwest. In the eastern coastal regions, EI gradually became the absolute dominant driver, demonstrating a rapid reduction effect. Additionally, a declining birth rate and rural-to-urban population migration have significantly amplified the driving effects of the population factor (RP) at a national scale. These findings, in conjunction with the disparities in geographic and socioeconomic development among provinces, can serve as a guiding framework for the development of a region-specific roadmap aimed at reducing agricultural non-CO2 GHG emissions.

Spatial and temporal evolution and drivers of GHG emissions from urban domestic wastewater treatment in China: a review at the provincial level

To mitigate greenhouse gas (GHG) emissions from the wastewater treatment industry, it is crucial to explore GHG emission patterns and propose useful measures. In this study, we use the Kaya model and LMDI decomposition method to analyze the changes in GHG emissions from urban domestic wastewater treatment at the provincial level and further explore the distribution characteristics and driving factors of urban domestic wastewater treatment GHG emissions across various years and regions. The results indicate the following: (1) In the temporal dimension, urban domestic wastewater treatment GHG emissions are increasing, from 21.0 MtCO2 in 2011 to 27.1 MtCO2 in 2020, with an average annual growth rate of 2.88%. The spatial distribution is high in the southeast and low in the northwest. There is variability in the spatial evolution trend of GHG emissions by province, with the growth rate becoming slower or even negative in Jiangsu, Zhejiang, and North China, while the average annual growth rate exceeds 25% in Inner Mongolia and Xinjiang. (2) According to the decomposition results of driving factors, economic scale is the dominant positive driver, and the positive contributions of TI and the population effect are limited. The sludge disposal structure is the main negative driver, and the EEI and technology have restricted negative contributions. (3) Based on the decomposition results, for major coastal GHG emitters, such as Guangdong and Shandong, it is necessary to invest capital and technology to continuously upgrade the wastewater treatment process and reduce non-CO2 emissions. Along with adopting circular economy schemes, local governments in the northwestern region should transform the traditional sludge disposal structure and optimize the power supply structure to increase carbon offset and reduce CO2 emissions. The findings suggest a low-carbon transformation path to support the industry's dual carbon goals.

Research on the path of building carbon peak in China based on LMDI decomposition and GA-BP model

The building sector contributes significantly to carbon emissions, impeding China's progress toward its 2030 carbon emissions peak target due to the limited utilization of renewable energy sources. This study aims to forecast the peak and timing of carbon emissions in China's construction industry to chart a low-carbon roadmap for the sector's future. Initially, an extended logarithmic mean divisia index (LMDI) decomposition model, based on the Kaya identity, is proposed to gauge the contribution levels of driving factors affecting building carbon intensity. Subsequently, a hybrid prediction model (IGA-BP) is constructed, employing an optimized two-hidden-layer neural network via a genetic algorithm, to forecast building carbon emissions and intensity. Additionally, four scenarios are outlined, each defining pathways to simulate emissions peak, carbon peak timing, and intensity within the Chinese building sector from 2020 to 2050. The research findings reveal: (1) The final emission factor of buildings primarily drives the surge in building carbon intensity, while the industrial structure stands as the most significant limiting factor. (2) Compared to alternative models, the proposed hybrid prediction model more effectively captures the evolution pattern of carbon emissions. (3) The prediction results indicate that China's building carbon intensity has reached its peak. Pathway 12 closely aligns with the sector's carbon emissions peak, projecting a peak value of 5.609 billion tons in 2029. To attain this pathway, China needs to develop more precise and feasible emission reduction strategies for its buildings. Overall, the research outcomes furnish robust references for decision-making in future efforts aimed at reducing building emissions.

Decoupling analysis and forecast of economic growth from electricity consumption in the Yangtze River Delta region, China

Decoupling economic growth from electricity consumption is essential for energy conservation and emission reduction. Firstly, this paper applies the LMDI decomposition model to analyze the driving factors of electricity consumption in the Yangtze River Delta region. Secondly, scenario analysis and Monte Carlo technique are combined to research the evolutionary trend of electricity consumption from 2020 to 2035, so as to further analyze the decoupling state. Finally, using nonparametric kernel density estimation, this paper studies the evolution trend of decoupling state from 2005 to 2035. The results show that (1) economic growth is the main factor that promotes the increase of total electricity consumption. Domestic intensity and population scale contribute to the increase in total electricity consumption. The primary factor inhibiting the increase of total electricity consumption is production intensity, while industrial structure and urbanization level contribute to the decrease in total electricity consumption. (2) From 2005 to 2035, the decoupling level has been optimizing on the whole, and the internal gap has also reduced, but there still exists obvious internal gap. (3) Under the three scenarios, the evolution trend of production and domestic electricity consumption is the same. During 2020-2035, the production and domestic electricity consumption both show an increasing trend, with the total electricity consumption under the baseline scenario being the highest, followed by the general and the enhanced electricity-saving scenario. Combined with the empirical results of this paper, some policy recommendations are proposed.

Research on the evolution mechanism and decoupling effect of China's carbon emissions from the perspective of green credit: based on system dynamics model and Tapio model

Green credit is an important topic in the study of green finance system, but it has not been combined with China's carbon emission reduction effect and industrial carbon emissions. This study takes different industries in China as research objects to explore the relationship between green credit and industrial carbon emissions. First, the LMDI decomposition model was used to decompose the driving factors of industrial carbon emissions, and the effects of green credit efficiency and scale on carbon emissions were obtained. Secondly, on this basis, a system dynamics model was established to predict the changing trend of carbon emissions in different industries. By setting different scenarios of green credit, the development and evolution trend of carbon emission system was simulated when parameters changed, and the Tapio decoupling model was further established to analyze the decoupling effect of green credit and carbon emissions under different scenarios. Finally, the research results show that the increase in the scale of green credit can effectively inhibit carbon emissions and has the greatest effect on carbon emissions of the secondary industry. The incentive policy of green credit can effectively encourage industrial upgrading and development. With the growth of the balance of green credit, green credit and carbon emissions gradually reach the best decoupling state. This study provides empirical evidence for the objective evaluation of the implementation effect of China's green credit policy and has important reference value for the improvement and development of future policies.

Multidimensional analysis of the regional inequalities in indirect carbon emissions from China's residential consumption

Residential indirect carbon emissions (RICE) are the major contributor to carbon emissions from the household sector. Regional RICE inequality has gradually become the focus of current issues. This paper has accounted for the RICE level of each province in China from 2010 to 2020 and assessed the RICE inequality at different regional scales employing the Theil index. Additionally, this paper presents a comprehensive analysis of RICE inequality across three dimensions: region, consumption category, and driving factors, illustrating the principal sources and determinants of RICE inequality. The results indicate the following: (1) RICE inequality in China is generally on a downward trend. (2) The gap between eastern China and the other regions is the dominant source of RICE inequality. (3) Residence consumption affects RICE inequality far more than other consumption categories. (4) Disposable income and the urban-rural structure of the population are the predominant factors affecting RICE inequality for most regions. The consumption propensity effect has a relatively pronounced impact on RICE inequality in the central and western regions. Based on the analysis, local governments ought to focus on economic construction, promote urbanization, and regulate the housing market to alleviate the RICE inequality.

Decoupling analysis between economic growth and aluminum cycle: From the perspective of aluminum use and carbon emissions

Increasing aluminum demand under the clean energy and low-carbon transformation background increases the fuzziness of relationships between economic growth and aluminum use or aluminum related carbon emissions. To figure this out, this paper established an aluminum use and carbon emissions integrated decoupling model within the framework of anthropogenic aluminum cycle. A material flow analysis (MFA) during 2000-2020 for China's aluminum cycle was firstly conducted to quantify both aluminum flow and carbon emissions in each aluminum life-cycle process. Then, this paper evaluated and decomposed the decoupling index of aluminum use-economy and carbon emission-economy via the LMDI decomposition model. Results show that: (1) secondary aluminum has not become effective supplement for primary aluminum in China; (2) the expansive negative decoupling state was the most prevalent state. The decoupling effects of carbon emission were better than that of aluminum use; (3) technology improvement was an important impactor to decoupling process but didn't offset the growth in aluminum consumption or carbon emissions at most of the time. The government and industry organizers should implement active countermeasures to stimulate aluminum companies developing technology to improve aluminum use efficiency and reduce carbon emissions.

Decoupling for a greener future: a spatio-temporal analysis of CO2 emissions and economic growth

Climate change mitigation is a pressing global challenge that requires reducing CO2 emissions without hindering economic growth. Using an extended Kaya identity, Logarithmic Mean Divisia Index (LMDI), and Tapio decoupling indicator, this paper investigates the spatio-temporal variations, drivers, and decoupling of CO2 emissions from economic growth in 150 countries from 1990 to 2019, considering regional disparities and income-based inequalities. The findings reveal increasing CO2 emissions between 1990 and 2019, with notable fluctuations in certain 5-year intervals. CO2 emission growth varied significantly by region, with countries like China, the USA, India, and Japan experiencing rapid increases. Economic growth emerged as the primary driver of CO2 emission growth, and its impact strengthened over time. Population growth also contributed significantly to CO2 emissions, particularly in middle- and low-income countries. The study identifies energy and carbon intensity as crucial mitigating factors that weaken CO2 emissions, offering hope for effective climate change mitigation. Furthermore, the degree of decoupling between economic growth and CO2 emissions varied among countries in the same region, with high-income countries demonstrating stronger decoupling compared to upper-middle-income countries, which accounted for 71% of global CO2 emission increase. These findings underline the imperative of accounting for income levels and regional differences in formulating CO2 emission mitigation strategies. Also, the study emphasizes the pressing necessity for cohesive global coordination to facilitate the transition toward a low-carbon economy. Such collaborative endeavors are paramount in our collective pursuit to combat climate change effectively, safeguarding the well-being and sustenance of our planet for future generations. As policymakers, it is imperative to integrate these insights into decision-making processes to chart a sustainable and resilient course forward.

Spatial evolution and decomposition of energy-related CO2 emissions in China's mining industry: from the perspective of regional heterogeneity

To ensure China's energy security, the mining industry faces increasing emissions reduction and energy conservation pressures. This study combined index and production-theoretical decomposition analyses to decompose the energy-related CO2 emissions in mining industry (ERCEMI) influencing factors into seven major effects and adopted a gravity model to dynamically visualize the transfer path and gravity distribution from 2000 to 2015. As investment effects were introduced into the decomposition analysis, the results fully considered the regional heterogeneity and spatiotemporal dynamics. The main findings were as follows: (i) a typical heavy emissions trend along the Heihe-Tengchong line, with a concentration of large ERCEMI values; (ii) the gravity center of ERCEMI had shifted to the southwest, and the migration trends were divided into three stages; (iii) the ERCEMI had strong regional heterogeneity, with a diffusion trend from north to south and shrinking from east to west; (iv) the potential energy intensity and investment efficiency effects had significantly inhibited the ERCEMI, while the investment scale had boosted it. Implications for regional layouts, energy intensity reductions, and investment optimization are discussed. This research provides a comprehensive regional analysis for ERCEMI reductions and the sustainable development of the mining industry and provides a reference for local industrial development planning.

Understanding material and energy use in the processes of decoupling CO2 emissions from economic growth

The share of emissions from materials has dramatically increased over the last decades and is projected to rise in the coming years. Therefore, understanding the environmental effect of materials becomes highly crucial, especially from the climate mitigation perspective. However, its effect on emissions is often overlooked and more attention is heavily paid to the energy-related policies. In this study, to address this shortcoming, we investigate the role of materials on the decoupling of carbon-dioxide emissions (CO2) from economic growth and compare it with the role of energy use in the world's top-19 emitting countries for the 1990-2019 period. Methodologically, using the logarithmic mean divisia index (LMDI) approach, we first decompose CO2 emissions into four effects based on the two different model specifications (materials and energy models). We secondly determine the impact decoupling status and efforts of countries with two different approaches: Tapio-based decoupling elasticity (TAPIO) and decoupling effort index (DEI). Our LMDI and TAPIO results show that material and energy-related efficiency effects have an inhibitory factor. However, the carbon intensity of materials has not contributed to CO2 emissions reduction and impact decoupling as much as the carbon intensity of energy has. DEI results indicate that while developed countries make relatively good progress towards decoupling, particularly after the Paris Agreement, developing countries need to further improve their mitigation efforts. Designing and implementing some policies only centering energy/material intensity or carbon intensity of energy might not be sufficient to achieve the decoupling. Both energy- and material-related strategies should be considered in harmony.

Decomposition of drivers and identification of decoupling states for the evolution of carbon emissions from energy consumption in China

As the largest energy consumer, China's control of carbon emissions from energy consumption plays a pivotal role in world climate governance. However, few studies have been conducted to explore the emission reduction pathways that promote a high level of synergy between China's economic growth and the " carbon peaking and carbon neutrality " goal from the perspective of energy consumption. Based on the measurement of energy consumption carbon emissions, this paper reveals the spatial and temporal distribution and evolution trends of carbon emissions in China at the national-provincial level. The multi-dimensional socio-economic factors such as R&D and urbanization are taken into account, and the LMDI model is used to decompose the driving effects of energy consumption carbon emissions at the national-provincial levels. Further, this paper combines the Tapio decoupling index with the LMDI model to decompose the decoupling states of China year by year and at the provincial level in four periods to explore the reasons for the change of carbon decoupling states. The results show that: (1) China's energy consumption carbon emissions grew at a high rate before 2013, and slowed down after that. There are significant differences in the scale and growth rate of carbon emissions among provinces, which can be classified into four types accordingly. (2) The R&D scale effect, urbanization effect, and population scale effect are the factors driving the growth of China's carbon emissions; while the energy structure effect, energy consumption industry structure effect, energy intensity effect, and R&D efficiency effect inhibit the growth of China's carbon emissions. (3) Weak decoupling is the most dominant decoupling state in China from 2003 to 2020, and the decoupling state varies significantly among provinces. According to the conclusions, this paper proposes targeted policy recommendations based on China's energy endowment.

The role of OFDI in home-country pollution: insights from LMDI and 3SLS approaches

Under the global climate crisis, harnessing investment for sustainable development is a practical and effective measure for international society. Based on the logarithmic mean Divisia index (LMDI) decomposition and three-stage least squares (3SLS) structural approaches, this study explores the home-country pollution reduction effect of Chinese OFDI activities using the city-level panel data from 2007 to 2019. The findings of this study indicate that (1) China has made a remarkable achievement in PM2.5 pollution reduction and governance, especially from the year 2012. (2) The OFDI activities can significantly decrease the home-country PM_2.5 pollution. With every 1% increase in OFDI flows, the overall pollution level will decrease by 0.76%. (3) Compared with the scale mechanism, the technology and composition mechanism effects of OFDI flows are more evident in addressing the home-country PM_2.5 pollution. With several related policy implications, this study may fill the lacuna of how to play the role of OFDI activities in the home country, thus promoting sustainable development in the next stage.

Decoupling analysis and peak projection of manufacturing CO2 emissions from the perspective of investment

Reducing carbon emissions has become an urgent task in China. As the category with the largest economic and emissions contribution to the industry, the carbon emissions research of the manufacturing industry is particularly important. This paper uses the LMDI method to decompose manufacturing carbon emissions into seven influencing factors (i.e., population, urbanization, economic development, investment share, energy intensity, energy structure and emission intensity), in order to explore the factors driving manufacturing carbon emissions during 2003-2018. Then, the paper analyzes the decoupling relationship between manufacturing investment and carbon emissions in 30 provinces. Finally, three scenarios are developed to project future manufacturing emissions at the provincial level up to 2035, and whether manufacturing emissions in 30 provinces can realize peak is discussed. The paper results in three main findings. First, we find that energy intensity played the most important role in decreasing the manufacturing emissions during the whole study period, while the economic development and investment share were the main effect promoting manufacturing carbon emissions. Second, China experienced a process from weak decoupling to strong decoupling between manufacturing invest and emissions. Third, China's manufacturing carbon emissions can only achieve the carbon peaking target in 2030 under the High scenario, and 7 provinces cannot reach the peak before 2035 under the three scenarios.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-023-03047-w.

Decomposition and scenario analysis of final demand embedded manufacturing consumption emissions: insights from the province-level data

In order to support the emissions reduction options in manufacturing industry effectively, it is necessary to quantify the final demand embedded manufacturing consumption (DEMC) emissions which can be estimated by converting intermediate manufacturing consumption into all final demand categories. Here, we quantify the DEMC emissions in China's 30 provinces during 2007-2017 using a multi-regional input-output (MRIO) model and the modified hypothetical extraction method (HEM). Then, we analyze impacts of four factors (including emissions multipliers, consumption structure, investment efficiency, and investment scale) on the DEMC emissions. Finally, considering a large driving effect of investment scale on manufacturing emissions, we conduct four scenarios to quantify the mitigation potential of DEMC emissions during 2020-2035. We find that from 2007 to 2012, the DMEC emissions increased doubled, while during 2012-2017, it decreased from 1217 to 634 Mt. The capital-intensive manufacturing and the labor-intensive manufacturing industries were main sources of intra- and inter-sectoral emissions, respectively. Investment scale was the main driver of the growth in DEMC emissions during 2007-2015, while it led to a reduction of DEMC emissions during 2015-2017. Emission multipliers had the largest positive impact on the reduction of DEMC emissions during the whole period. Consumption structure increased DEMC emissions during 2007-2012, while with the consumption structure shift towards knowledge-intensive manufacturing industry, it induced a reduction of DEMC emissions during 2012-2017. Moreover, implementing an integrated mitigation measures (including reducing emissions multipliers, decreasing investment efficiency, and adjusting consumption structure) could help China to realize the emissions peaking target. However, there are still 8 provinces whose DEMC emissions are unlikely to peak before 2030.

Decoupling and scenario analysis of economy-emissions pattern in China's 30 provinces

The tension between reducing CO₂ emissions and economic growth has become increasingly prominent in recent years, while China is vigorously promoting ecological civilization to achieve sustainable development. However, the factors influencing China's current economic emission nexus at the regional and provincial levels and the sustainability of the strong decoupling state remain unclear. We analyze the decoupling of emissions at the national and provincial levels of the Chinese economy from the perspective of historical patterns and current drivers from 1997 to 2019. Also, we developed three scenarios (i.e., pessimistic, median, and optimistic scenarios) to analyze the impact of decoupling relationship changes. We find that China's national decoupling relationship has eased since 1997, but it has not yet reached the ideal state, with provinces mainly exhibiting weak decoupling. The EKC hypothesis is tested for the whole country and 30 provinces and finds that 15 provinces have two turning points, 13 provinces have one turning point, and the others have no turning point. Based on the scenario analysis, the total emissions in the pessimistic scenario (S1) without any improvement of decoupling would increase by 73.97% compared to the level of 2019. However, the total emissions in the optimistic scenario (S3), in which all provinces obtained strong decoupling, are almost half of the level of 2019. This is mainly from the reduction of emissions in the western less developed regions (e.g., Shanxi, Inner Mongolia, and Xinjiang) and developed coastal regions (e.g., Jiangsu and Shandong). On the basis of the results of factor analysis, we put forward policy recommendations for expanding electrification, optimizing industrial structure, and promoting technological innovation.

What are the determinants of wastewater discharge reduction in China? Decomposition analysis by LMDI

Wastewater discharge reduction (WDR) is a key breakthrough point for China's environmental protection. Based on China's 30 provincial data from 2011 to 2017, this paper applied the logarithmic mean Divisia index (LMDI) method to clarify the determinants of WDR at national, regional, and provincial levels. Except for wastewater discharge factor, economic development, and total population, four innovative factors, total water application intensity, water environment cost, water treatment industry development level, and drainage infrastructure investment scale were first proposed in this study. The results indicated that from 2011 to 2017, at the national level, total water application intensity and water treatment industry development level were dominant contributors to WDR, while other factors all inhibited WDR. At the regional level, the results of wastewater discharge factor, economic development, and water environment cost were similar to the national level. The drainage infrastructure investment scale had a positive effect on WDR in Northeast and South China while having a negative effect on other regions. And except for Northeast China, the water treatment industry development level promoted WRD, while the total population inhibited WDR. Finally, the determinants of WDR at the provincial level were investigated. On this basis, targeted corresponding policies were provided in this paper.

When will China's total water consumption reach the turning point? EKC simulation and influencing factors

The turning point of China's total water consumption is very important for the understanding of the evolution trend of total water consumption and the formulation of water conservation policies. Based on the environmental Kuznets curve (EKC) model, this paper verifies the shape of water consumption Kuznets curve. Scenario analysis and Monte Carlo simulation are combined for the first time to predict water consumption Kuznets curve. The LMDI model is used to decompose the driving factors of the evolution of total water consumption, and the STIRPAT model is expanded to explore the influence mechanism of total water consumption. The results show the following: (1) The theoretical water consumption Kuznets curve exists, and the turning point is 26,448 yuan RMB (in around 2013). (2) Based on the multiple driving factors (water intensity, per capita GDP, and population) and multiple scenarios (baseline scenario, target scenario, and 2 adjusted scenarios), 32 scenarios are designed in this paper; in the S1-S8, the turning point still appeared near 2013; the curves under the S11-S14, S16, and S25-S32 were inverted U-shaped, and the turning point was 48,728 yuan RMB (in around 2025); and in the S9, S10, and S15, the curve shows an upward trend; in the S17-S24, the curve has a rising-falling-rising characteristic. (3) Domestic effect and ecological effect both play a role in promoting the total water consumption, while the production effect is in an inverted N-shaped. Economic growth has always promoted the increase in industrial and agricultural water consumption, and the role of population size is relatively weak. The intensity of production water consumption has always promoted reduction in industrial and agricultural water consumption. Industrial water intensity and industrial structure are the primary and secondary factors that promote the decline of production intensity. (4) The per capita GDP has the largest contribution to total water consumption, followed by the water intensity, and the industrial structure has the least impact. The population has a negative impact. Based on this, a number of policy implications are obtained.

The influencing factors of carbon emissions in the railway transportation industry based on extended LMDI decomposition method: evidence from the BRIC countries

In the twenty-first century, global warming and other environmental issues have become the focus of international attention. The total generation of carbon emissions for the railway transportation industry in the BRIC countries (Brazil, Russia, Indian and China) accounted for 25.73% of the global carbon emissions in this industry during 2017. Therefore, it is necessary to identify the influencing factors of carbon emission in the railway transportation industry for the BRIC, in order to better control and reduce carbon emissions and to achieve the global goal of "net-zero emission." The logarithmic mean divisia index (LMDI) decomposition method was used to examine the factors that influenced carbon emissions from the railway transportation industry in the BRIC from 1997 to 2017. According to the findings, the total carbon emissions of the railway transportation industry in BRIC were 60.92 million tons in 2017, increased by 98.62% compared to 1997. The factor of economic output effect has contributed positively to the increase in carbon emissions in all identified countries. However, the effect of population size effect, energy structure, and transportation intensity effect for carbon emission demonstrated heterogeneity in BRIC. In addition, policy suggestions are put forward for the reduction of carbon emissions from the railway transportation industry in BRIC.

Decoupling trend and emission reduction potential of CO2 emissions from China's petrochemical industry

This paper aims to study the decoupling status and emission reduction potential of China's petrochemical industry from 1996 to 2019. Firstly, the IPCC method is used to calculate the CO₂ emissions of the petrochemical industry in China, then the logarithmic mean Divisia index (LMDI) method is used to identify the influencing factors of CO₂ emissions, then the decoupling index is constructed to analyze the dependence of economic development on CO₂ emissions, and finally the emission reduction potential model is established by using the influencing factors to reflect the CO₂ emission reduction potential of the petrochemical industry. The results reveal that (1) the CO₂ emissions can be divided into two stages of slow decline (1996-2000), (2015-2019), and one stage of rapid growth (2000-2015). (2) The energy intensity effect is the most effective factor to restrain CO₂ emission, the economic growth effect is the key factor to promote CO₂ emission. (3) From 1996 to 2019, there was a weak decoupling relationship between CO₂ emission of petrochemical industry and economic development. Strong decoupling only occurred in 1996-2000 and 2015-2019. The CO₂ emissions show only three decoupling score: I, II, and III. (4) CO₂ mitigation occurred in four sub periods: 1996-2000, 2005-2010, 2010-2015, and 2015-2019. Therefore, the government should establish an energy-saving and environment-friendly industrial production system, intensify the use of clean energy, and optimize the labor force structure. It not only effectively strengthens the decoupling between the petrochemical industry and economic development, but also provides an empirical example for the carbon emission reduction and economic sustainable development of the petrochemical industry in other countries in the world.

Analysing the Effect of Energy Intensity on Carbon Emission Reduction in Beijing

Beijing has experienced rapid economic development since the reforms and opening up. However, the traditional development model based on excessive energy consumption has posed great challenges to the ecological environment. To curb environmental degradation and achieve sustainable social development, Beijing has proposed to achieve carbon neutrality by 2050. As an important indicator of energy consumption, it is necessary to clarify how energy intensity (EI) affects carbon emissions (CE) to achieve carbon neutrality in Beijing by 2050. This study first decomposes the drivers of CE in Beijing from 2010 to 2020 using the logarithmic mean Divisia index (LMDI) method and comparatively analyses the impact of EI on CE. Then, the spatial Dubin model (SDM) is used to analyse the spatial spillover effect of EI on CE at the regional level. Finally, the macro moderating role of economic development in the effect of EI on CE is analysed. The results show that the effect of EI has been the main driver of CE reduction in Beijing. Among the industrial sectors, manufacturing and transportation have had the greatest success in reducing CE through EI reduction. At the regional level, there is a spatial spillover effect of EI on CE, and the effect of carbon reduction through the spillover effect of EI is greater than the direct effect of EI. Economic factors have an enhanced moderating effect on the process of EI affecting CE, and this moderating effect has threshold properties.

Analysis of carbon emission drivers of secondary industries in Energy "Golden Triangle" area based on LMDI and two-dimensional decoupling model

As an essential energy and chemical base in China, carbon reduction in the Energy "Golden Triangle" (EGT) area is significant. This paper used the logarithmic mean Divisia index (LMDI) method to analyze the drivers of carbon emissions from secondary industry energy consumption (CESEC) in EGT from 2005 to 2019 and then used the GM (1,1) method to simulate carbon emissions in 2030. Meanwhile, the decoupling relationship between carbon emissions and economic development was also analyzed using the two-dimensional decoupling model to test the effectiveness of carbon reduction by the region's government. This paper showed the following: (1) CESEC in the EGT area increased from 1.89×10⁸t to 2.617×10⁸ t; (2) the economic output effect is the main factor influencing carbon emissions in the EGT area, followed by population effect and energy structure effect, while energy intensity effect mitigates carbon emissions; and (3) CESEC will peak at 12.362×10⁸t in 2030, leaving an arduous task on carbon reduction. The two-dimensional decoupling condition between carbon emissions and economic growth in the EGT area is low level-weak decoupling (WD-LE) for 2005-2019. The decoupling condition in Yulin and Ningdong is concentrated in low level-expansion connection (EC-LE) and low level-weak decoupling (WD-LE). Furthermore, Erdos reached high level-expansion negative decoupling (END-HE) condition during 2015-2019. Based on the above findings, a low-carbon development strategy for EGT should consider improving emission reduction technologies for high-carbon energy sources like coal, adjusting the energy consumption structure and seeking government policy support for carbon reduction.

A nexus study of carbon emissions and financial development in China using the decoupling analysis

Investigating the linkage between financial development (FD) and carbon emissions is important for mitigating climate change. Nevertheless, there is a scarcity of studies investigating how carbon emissions decouple from FD. Here, we investigate the relationship between FD and carbon emissions by using the decoupling model based on cross-province data of China during 2000-2019. Then, we use the decomposition method to analyze the nine drivers of decoupling elasticity of FD and CO₂ emissions. We found that China experienced weak decoupling and strong negative decoupling in most years. Only the finance develops at a very high level; the FD had spare capacities to promote the reduction in the carbon emissions. For example, several developed provinces (e.g., Tianjin, Zhejiang, Guangdong) realized strong decoupling after 2012. The reduction in energy intensity and the increase of foreign direct investment promoted the decoupling of FD from carbon emissions. During the financial recession period, developing a bank-based financial market helped the emissions reduction. Once financial crisis is overcome, developing a market-based financial market promoted the decoupling of FD from emissions. This is because that with the fast FD, the development of stock market contributed to emission reductions through technological improvement, while the bank loans inhibited the decoupling process through the expansion of capital-labor inputs. Overall, these results help in the assessment of the emissions impacts of FD and in addressing climate change problems.

Heterogeneity and Decomposition Analysis of Manufacturing Carbon Dioxide Emissions in China's Post-Industrial Innovative Megacity Shenzhen

Effectively reducing manufacturing carbon dioxide (CO₂) emissions is a vital strategy for China to curb its rapidly rising carbon footprint. Features of such a reduction in manufacturing include an increase in the share of high-tech manufacturing and optimization of the energy consumption structure. This study aims to analyze the case of Shenzhen, a unique post-industrial innovative manufacturing megacity, for its leading experience in China's manufacturing transition. Disaggregated manufacturing emissions data of Shenzhen, including 27 sub-sectors in four categories, were collected, and driving factors were identified by the logarithmic mean Divisia index (LMDI) method. The results suggest that: (1) CO₂ emissions from Shenzhen's manufacturing show a phased difference between 2008-2012 and 2012-2020. CO₂ emissions embodied in electricity consumption have increased by over 30% in the former period and have remained stable at a high level of over 90%. (2) Significant heterogeneity of CO₂ emissions in various manufacturing sectors is revealed, with the largest emissions sources being factories that make communication equipment, computers, and other electronic equipment. (3) Lower carbon intensity is the primary factor in reducing CO₂ emissions, while the economic activity effect of manufacturing possesses a stimulating impact. (4) The marginal impact of restructuring on CO₂ emissions is insignificant since the manufacturing and energy structures of Shenzhen have been upgraded to a low carbon level. Therefore, strengthening the power saving management and improving the energy efficiency of the manufacturing, rather than optimizing the manufacturing and final energy structures, will be a necessary potential solution to the problem of how to reduce CO₂ emissions in Shenzhen's manufacturing.

Carbon emission of China's power industry: driving factors and emission reduction path

The low-carbon development of power industry is the key to low-carbon development of the whole society. In order to determine appropriate and feasible emission reduction policies, it is necessary to identify the contribution of different drivers to the change of carbon emissions in China's power sector and to simulate the potential evolution trend of carbon emissions. This paper constructs LMDI model to analyze the driving factors of carbon emission changes in China's power industry from 2000 to 2018 and uses Monte Carlo algorithm to simulate the evolution trend of carbon emission under different scenarios. We can find (1) economic output effect reached 3.817 billion tons from 2000 to 2018, which was the primary factor to increase the carbon emission. Population scale effect reached 251million tons, which had a weak promotion impact on carbon emission. (2) Conversion efficiency effect played a role in restraining carbon emissions, reaching 699 million tons from 2000 to 2018. (3) Emission factor effect and power intensity effect have obvious volatility. The power structure effect showed great volatility before 2013 and mainly played a role in restraining carbon emission after 2013. (4) Under the baseline scenario, the carbon emission of China's power industry shows a growth trend. Under green development scenario and enhanced carbon reduction scenario, the carbon emission shows a trend of first increasing and then decreasing.

Decomposition of residential electricity-related CO2 emissions in China, a spatial-temporal study

The COVID-19 pandemic brings a surge in household electricity consumption, thereby enabling extensive research interest on residential carbon emissions as one of the hot topics in carbon reduction. However, research on spatial-temporal driving forces for the increase of residential CO₂ emissions between regions still remains unknown in terms of emissions mitigation in post-pandemic era. Therefore, we studied the residential CO₂ emissions from the electricity consumption of China during the period 1997-2019. Afterward, the regional specified production emission factors, combining with electricity use pattern, living standard and household size, were modelled to reveal the spatial-temporal driving forces at national and provincial scales. We observed that the national residential electricity-related CO₂ increased from 1997 to 2013, before fluctuating to a peak in 2019. Guangdong, Shandong and Jiangsu, from East China were the top emitters with 27% of the national scale. The decomposition results showed that the income improvement was the primary driving force behind the emission increase in most provinces, while the household size and production emission effects were the main negative effects. For the spatial decomposition, differences in the total households between regions further widen the gaps of total emissions. At the provincial scale of temporal decomposition, eastern developed regions exhibited the most significant decrease in production emissions. In contrast, electricity intensity effect showed negative emission influences in the east and central regions, and positive in north-eastern and western China. The research identified the different incremental patterns of residential electricity-related CO₂ emissions in various Chinese provinces, thereby providing scientific ways to save energy and reduce emissions.

Decomposition and decoupling analysis of carbon emissions from agricultural economic growth in China's Yangtze River economic belt

In this study, carbon emissions from agricultural energy consumption (CEAEC) are fully analyzed using data from the Yangtze River Economic Belt (YEB) between 2000 and 2017. First, generalized LMDI is adopted to decompose the drivers of CEAEC into five components. Then, the decoupling indicator and the decoupling effort indicator are constructed to quantify the decoupling degrees and examine the government's emission reduction efforts, respectively. The results show that (1) CEAEC in the YEB has shown a phased increase, reaching a peak at 1732.2510⁴t in 2012. Except for some decreases found in Shanghai, Chongqing, and Guizhou, it is shown that all provinces' CEAEC have risen to varying degrees. In contrast, the intensity of CEAEC in the YEB has been declining since 2005. (2) The economic output effect acts as the major contributor to the growth of CEAEC, followed by the population effect. In contrast, both the energy intensity effect and the energy structure effect are the primary reasons for reductions in CEAEC. The spatial difference in CEAEC in the YEB increased significantly from 2000 to 2017. (3) There was an alternating change from decoupling to coupling and then to negative decoupling from 2000 to 2017. Based on the conclusions mentioned above, it is proposed that the formulation of low-carbon agricultural development strategies should consider the structural adjustment of agricultural energy consumption and the advancements of agricultural technology.

Impacts of regional development on emissions in China's transport sector

The CO₂ emissions in China's transport sector increased from 349.00 Mt in 2005 to 723.87 Mt in 2017. Thus, a number of climate change policies are being implemented to adjust regional structure and to decrease the emissions in China's transport sector at the regional level. However, few studies explored the impact of changes in regional structure (that is, measured regional share of the added value of transport sector) on emissions in China's transport sector. Therefore, based on the Kaya identity and LMDI analysis, we decompose 8 factors (including carbon intensity, energy structure, energy intensity, turnover intensity, transport intensity, regional structure, per-capita traffic activity, and population size) to analyze the driving factors of emissions in China's transport sector. The period 1997-2017 is divided into four phases according to the growth rate of emissions. The results show that regional structure increased CO₂ emissions in China's transport sector between 2013 and 2017. The fast transport development in the Southwest region, reflected by the increase in the share of total transport value added, resulted in emissions growth during 2013-2017. Moreover, the change in the growth rate of the regional transport sector's value added is positively correlated with the change in the regional share of value added, which is positively correlated with the change in regional emissions.

Spatiotemporal dynamic differences of energy-related CO2 emissions and the related driven factors in six regions of China during two decades

Carbon neutrality lays out a grand blueprint for carbon emission reduction and climate governance in China. How to reduce energy consumption is the key to achieving this goal. The economic development and energy consumption show a very large gap at the provincial level, and this paper divides China into six regions (North, Northeast, East, Mid-South, Southwest, and Northwest) and analyzes the dynamic changes and reveals the driving factors that have affected CO₂ emission changes from 1997 to 2017. Then, the driving forces including energy intensity, energy structure, energy efficiency, economic activity, and population scale were discussed employing the logarithmic mean Divisia index (LMDI) based on provincial panel data. The results show that CO₂ emissions from energy consumption show an upward trend, from 4145 Mt in 1997 to 13,250 Mt in 2017, with an annual average growth rate of 1.06%; coal consumption is the main source of CO₂ emission. The regions with the highest proportion of CO₂ emissions are the East and North, which account for 50% of total emissions. China's CO₂ emissions from energy consumption, coal consumption, and output have shown significant spatial autocorrelation at the provincial scale. According to coal consumption, energy consumption CO₂ emissions are divided into three stages: phase I (1997-2002), the increase in CO₂ emissions in six regions was attributed to significant and positive impacts of energy intensity, economic activity, and population scale, the effects of which exceeded those of the energy structure and energy efficiency; phase II (2003-2012), the economic activity effect on CO₂ emissions was highest in the East region, followed by the North and Mid-South regions; phase III (2013-2017), the East, Mid-South, and Southwest regions of China were dominated by the positive effects of energy intensity, economic activity, and population scale. The major driver of CO₂ emissions is economic activity; the energy efficiency effect is an important inhibitory factor. Regional economic development and energy consumption in China are unbalanced; we conclude that differentiated emission reduction measures should be of particular concern for policymakers.

Assessing the sustainability of ecosystems over fourteen years of cultivation in Longnan City of China based on emergy analysis method

Currently, the contradiction between the limited resources of China's cultivated ecosystems and population growth is becoming increasingly evident, and the negative impacts on the environment and human activities need to be curbed. Therefore, it is crucial to quantify the sustainability of cultivated ecosystems and determine these driving factors that affect their development. This study used the emergy method to quantify the input/output flow and sustainable development of the cultivated land ecosystem in Longnan City, combined with the logarithmic mean divisia index decomposition analysis (LMDI) method to evaluate the driving factors of sustainable development in the region. The results demonstrate that from 2004 to 2017, the total emergy input and output of Longnan City showed an upward trend, and non-renewable resources (N) were always in a dominant state in the total emergy (T) input, and their proportion rose from 59.69% to 66.92%. The emergy sustainability index (ESI) is less than 1, and the environmental pressure of the system is relatively higher. Comprehensive emergy production ratio (EPR), emergy investment ratio (EIR), the renewable fraction (R%), emergy yield ratio (EYR) and environmental load ratio (ELR), showed that the agricultural ecological economy in Longnan still has great development potential, and clean energy should be developed as far as possible to replace fossil fuels in future planning. LMDI results showed that the intensity factor ΔY^'A is the main driving factor for the positive development of ESI. The government's ecological protection requirements can reduce waste emissions through reasonable farming system and advocating the use of organic fertilizer, so as to achieve the purpose of improving crop yield. Vigorous development of green ecological agricultural production patterns can improve the sustainability of arable ecosystems. This study can provide a theoretical basis for the sustainable development of cultivated ecosystems and the formulation of related agricultural production measures.

Driving factors of carbon emissions in China's municipalities: a LMDI approach

China, as the world's largest carbon dioxide emitter, is bound to assume the important responsibility of energy conservation and emission reduction. To this end, each city, led by representative municipalities, must enhance efforts in carbon emission reduction to jointly realize China's low-carbon transition. Taking four representative municipalities, namely, Beijing, Tianjin, Shanghai, and Chongqing as the case cities, this paper establishes a decomposition analysis for the driving factors of carbon emissions by applying the LMDI method covering data from 2007 to 2017. Kaya identity is used to decompose the effects into eight driving factors: GDP effect, industrial structure effect, energy intensity effect, overall energy structure effect, population effect, urbanization effect, per capita energy consumption effect, urban and rural energy structure effect. The results show that at the municipality level, the driving factors that contribute to carbon emissions are the GDP growth effect and the population effect, with the former still being the most important factor in the municipalities with faster economic growth; and industrial structure effect is the most important factor that inhibits carbon emissions, followed by energy structure effect. This paper considers the driving factors of both the production side and the residential consumption side from the city level. The research reveals the main driving factors that effect the carbon emissions of megacities in developing countries, and highlights the leading role of megacities in terms of carbon emission reduction in China and even the world. The paper thereby puts forward policy implications for China's economic policies.

Drivers of the increasing water footprint in Africa: The food consumption perspective

In Africa, water resources pervade multiple sustainable development goals (SDGs), which mainly focus on eliminating poverty (SDG 1) and hunger (SDG 2), promoting good health and well-being (SDG 3) and supporting clean water and sanitation (SDG 6). Africa's water scarcity problems have been worsened by population growth and climate change. Agriculture is the largest consumer of water in Africa, and a clear understanding of the water-food nexus is necessary to effectively alleviate water-related pressures on food security. Water footprint (WF) accounts and decompositions provide insights into water management planning for policy-makers. We investigated the WF of food consumption from 2000 to 2018 in 23 African countries and used the logarithmic mean Divisia index (LMDI) to decompose its driving forces into consumption structure, per capita food consumption, water intensity and population effect. The WF of food consumption increased from 609.8 km³ in 2000 to 1212.9 km³ in 2018, with an average annual growth rate of 3.7%. The population effect contributed most to this change (64.6%), followed by per capita food consumption (28.3%) and consumption structure (7.1%). Cereals (46.7%) and livestock (24.4%) were the major contributors to the increase in the total WF. Our findings highlight that controlling population growth and improving water efficiency are effective measures to relieve water-related pressures on food consumption. However, a healthy dietary structure must also be promoted because Africa's current dietary energy level is below the global average. Moreover, nine countries in the research area have an inadequate supply of dietary energy; this will inevitably drive the WF of food, as calories increase and diets change. This study is helpful for understanding the water-food nexus in Africa and provides strategies to conserve water and enhance food production.

Decomposition and decoupling research of Chinese power sector carbon emissions through the consumption accounting principle

The Logarithmic Mean Divisia Index (LMDI) model is applied to study Chinese national and regional power sector carbon emission changes through consumption side from 2003 to 2017, and regional power sector carbon emissions are estimated through the production and consumption accounting principle. The two-factor ANOVA and one-factor ANOVA are used to compare the differences of regional power sector carbon emissions through the two principles. In addition, the Tapio decoupling analysis model is used to investigate the decoupling state between carbon emissions of power sector and the corresponding driving forces through the consumption side. There are several results: (1) Through the two different principles, regional power sector carbon emissions are statistically significant, yet national power sector carbon emissions are not statistically significant; (2) the main factors contributing to the power sector carbon emission growth are economic scale effect and income level effect, and the main restraining factors are electricity consumption carbon intensity effect and production sector electricity intensity effect; (3) the highest contribution effect to the decoupling indexes between various influencing factors and power sector carbon emissions was scale effect, and technical effect had the second largest contribution value; (4) in 2003-2017, economic scale effect was the first significant factor causing the difference of regional power sector carbon emissions, followed by production sector electricity intensity effect and electricity consumption carbon intensity through the regional decomposition analysis. Finally, this paper gives some targeted suggestions for the low-carbon development of the power sector through national and regional perspectives.

Decomposition analysis of the decoupling and driving factors of municipal solid waste: Taking China as an example

As one type of debt 'borrowed' from nature, municipal solid waste (MSW) can be influenced by financing debt. Taking China as an example, an improved Logarithmic Mean Divisia Index (LMDI) model, together with the Tapio decoupling model, is developed to analyze the impact of private debt on MSW generation and the relationship between MSW and economic growth. The results show that the debt-income ratio promotes MSW generation and the output efficiency of debt inhibits MSW generation. Second, the linkage relationship between GDP growth and MSW shows three states: strong decoupling, expansion coupling and weak decoupling. The MSW generation per unit of GDP and the output efficiency of debt are the main contributors to the change of decoupling state. Third, implementing a MSW classification measure can greatly reduce the quantity of MSW removed and transported and improve the decoupling state. By 2035, deleveraging scenario and economic growth slowdown scenario can reduce MSW removal and transportation quantities by 765 and 1080 million tons, respectively. It is worth noting that negative population growth worsens decoupling while curbing MSW. The results provide a new perspective for the realization of MSW reduction and some sound policies are formulated to improve MSW management.

Measurement and driving factors of grey water footprint efficiency in Yangtze River Basin

Water shortages and poor water quality have become an urgent problem that is constraining the sustainable development of China. Grey water has been found to bring greater stress on the water supply than freshwater consumption, and the grey water footprint (GWF) has received significant attention as a comprehensive indicator to assess wastewater pollution. In this study, we analysed the grey water footprint in the Yangtze River Basin from 2003 to 2017 and established a Logarithmic mean divisia index (LMDI) model to decompose the grey water footprint efficiency into six key factors. Our findings are as follows: (1) The average grey water footprint (AGWF) in the central regions was 40% higher than eastern region and 172% higher than western region; (2) Economic effects and capital deepening effects are the main factors affecting positive changes in grey water footprint efficiency; (3) Based on an analysis of the driving factors of greywater footprint efficiency in each province, we conducted a territorial classification according to the primary driving factors in each province. Our results reflect the spatial distribution characteristics of the influencing factors on the grey water footprint effect in the Yangtze River Basin and will enable the government to formulate relevant policies for each subregion.

Analysis of the drivers of CO2 emissions and ecological footprint growth in Australia

This paper investigates the determinants of environmental degradation in Australia from 1990 to 2017, using ecological footprint analysis and the well-established logarithmic mean Divisia index (LMDI) decomposition method. Additionally, decoupling factor analysis was performed to examine the link between environment related variables (CO₂ emissions and ecological footprint) and their determinants such as real income and population. The decomposition analysis considered the impact of five different factors on CO₂ emissions: income effect, population, energy intensity, energy structure, and carbon intensity. For decoupling factor analysis, the link between ecological footprint and its two determinants, real income and population, was examined. Furthermore, the possible decoupling between CO₂ emissions and these determinants was also analyzed, because CO₂ emissions are the main cause of the country's increasing ecological footprint. The present study has a more comprehensive approach because it analyzes the factors affecting environmental degradation in Australia by assigning two proxies (CO₂ emissions and ecological footprint) as dependent variables. The results confirmed that Australia's ecological reserve substantially declined over the past three decades due to deforestation and energy industries. The LMDI results demonstrated that income effect, population, and carbon intensity were the main factors that raised Australia's CO₂ emissions, whereas the energy intensity factor substantially curbed them. The reducing impact of energy structure on CO₂ emissions was minimal; thus, Australia was not able to prevent an upward trend in CO₂ emissions. Lastly, an analysis of Australia's CO₂ emissions according to economic activities was conducted for the period between 1990 and 2017 in order to understand other factors that may have affected environmental sustainability.

Decomposition analysis of industrial pollutant emissions in cities of Jiangsu based on the LMDI method

Cities are faced with various kinds of pollution issues in the process of economic development, among which industrial pollution has become the most terrifying environmental issue in recent years, so that industrial pollution control should be emphasized. Finding out the key factors influencing industrial pollutant emissions is the basis of taking corresponding measures. Previous studies only focused on one pollutant without a comparative analysis of the contribution of influencing factors to multiple pollutants. Therefore, this study aims to identify the key influencing factors of industrial pollutants in Nanjing, Suzhou, Xuzhou, and Taizhou in Jiangsu Province during the years 2008-2018 by using the logarithmic mean Divisia index (LMDI) method. The results from decomposition indicate the following. (1) Emission intensity (EI) and energy efficiency (EE) are negative factors for decreasing industrial pollutant emissions, while the economic output (EO) and population (P) are positive factors for increasing industrial pollutant emissions. (2) Emission intensity has the most significant influence to industrial wastewater in decreasing emissions; energy efficiency makes the biggest contribution to industrial solid waste in decreasing emissions, economic output and population contribute the most to industrial solid waste in increasing emissions. (3) Nanjing has the highest contribution rate of emission intensity and population, and the contribution rate of energy efficiency and economic output to Taizhou is the highest. Identifying the key driving factors of different pollutants can serve as evidence and guidance for urban environmental governance, therefore reducing emissions ulteriorly, and achieving sustainable development.

Decoupling and Decomposition Analysis of Agricultural Carbon Emissions: Evidence from Heilongjiang Province, China

Ensuring food security and curbing agricultural carbon emissions are both global policy goals. The evaluation of the relationship between grain production and agricultural carbon emissions is important for carbon emission reduction policymaking. This paper took Heilongjiang province, the largest grain-producing province in China, as a case study, estimated its grain production-induced carbon emissions, and examined the nexus between grain production and agricultural carbon emissions from 2000 to 2018, using decoupling and decomposition analyses. The results of decoupling analysis showed that weak decoupling occurred for half of the study period; however, the decoupling state and coupling state occurred alternately, and there was no definite evolving path from coupling to decoupling. Using the log mean Divisia index (LMDI) method, we decomposed the changes in agricultural carbon emissions into four factors: agricultural economy, agricultural carbon emission intensity, agricultural structure, and agricultural labor force effects. The results showed that the agricultural economic effect was the most significant driving factor for increasing agricultural carbon emissions, while the agricultural carbon emission intensity effect played a key inhibiting role. Further integrating decoupling analysis with decomposition analysis, we found that a low-carbon grain production mode began to take shape in Heilongjiang province after 2008, and the existing environmental policies had strong timeliness and weak persistence, probably due to the lack of long-term incentives for farmers. Finally, we suggested that formulating environmental policy should encourage farmers to adopt environmentally friendly production modes and technologies through taxation, subsidies, and other economic means to achieve low-carbon agricultural goals in China.

Spatial-temporal analysis of China's carbon intensity: a ST-IDA decomposition based on energy input-output table

It is of crucial importance to identify the driving factors for emission changes since China's commitment to reduce carbon intensity in 2009. Hence, the spatial-temporal variation of carbon intensity of China's 30 provinces from 2010 to 2017 is explored by applying a Spatial-temporal Index decomposition analysis (ST-IDA) model combined with energy input-output analysis. Industrial structure, energy intensity, energy structure, and carbon emission coefficient are identified as driving factors; simultaneously, a new factor, energy conversion efficiency, is also introduced based on the energy input-output analysis, which is of significance as China is vigourously pushing electricitification. The results show that the carbon intensity of economic sectors in most provinces declined from 2010 to 2017. Energy intensity is the biggest contributor to both the temporal decline of carbon intensity and its spatial difference for economic sectors, followed by industrial structure, energy conversion efficiency, energy structure and carbon emission coefficient, while the rank of inhibition of each factor is the same as above. Meanwhile, the carbon intensity of the residential sector is mainly affected by per capita GDP and per capita energy consumption. Related policy suggestions are given.

On the driving factors of China's provincial carbon emission from the view of periods and groups

China is the largest carbon emitter in the world. Understanding carbon emissions of China, especially at the provincial level, will help identify the critical factors behind carbon emissions and effectively implement carbon emission reduction measures. There are significant achievements in the study of carbon emissions of China's provinces. However, there is a gap for improvement in the study from periods and groups' perspectives using a decomposition-clustering method. This paper adopts the Logarithmic Mean Divisia Index (LMDI) to decompose each province's carbon emissions, introduces the elbow and K-means methods to cluster provinces based on the driving factors of decomposition, and analyzes the driving factors of carbon emissions from the view of groups and periods. By analyzing the carbon emissions data of 28 provinces in China from 1998 to 2018, a breakthrough has been found that economic activities and energy intensity were the main driving factors of carbon emissions. Some possible countermeasures, such as optimizing the industrial structure and the energy structure, significantly increasing clean energy consumption, would receive effective carbon emission reduction feedback. The results provide better decision-making support for emission reduction policies in China and contribute to global climate change issues.

Crop Production and Agricultural Carbon Emissions: Relationship Diagnosis and Decomposition Analysis

Modern agriculture contributes significantly to greenhouse gas emissions, and agriculture has become the second biggest source of carbon emissions in China. In this context, it is necessary for China to study the nexus of agricultural economic growth and carbon emissions. Taking Jilin province as an example, this paper applied the environmental Kuznets curve (EKC) hypothesis and a decoupling analysis to examine the relationship between crop production and agricultural carbon emissions during 2000–2018, and it further provided a decomposition analysis of the changes in agricultural carbon emissions using the log mean Divisia index (LMDI) method. The results were as follows: (1) Based on the results of CO₂ EKC estimation, an N-shaped EKC was found; in particular, the upward trend in agricultural carbon emissions has not changed recently. (2) According to the results of the decoupling analysis, expansive coupling occurred for 9 years, which was followed by weak decoupling for 5 years, and strong decoupling and strong coupling occurred for 2 years each. There was no stable evolutionary path from coupling to decoupling, and this has remained true recently. (3) We used the LMDI method to decompose the driving factors of agricultural carbon emissions into four factors: the agricultural carbon emission intensity effect, structure effect, economic effect, and labor force effect. From a policymaking perspective, we integrated the results of both the EKC and the decoupling analysis and conducted a detailed decomposition analysis, focusing on several key time points. Agricultural economic growth was found to have played a significant role on many occasions in the increase in agricultural carbon emissions, while agricultural carbon emission intensity was important to the decline in agricultural carbon emissions. Specifically, the four factors’ driving direction in the context of agricultural carbon emissions was not stable. We also found that the change in agricultural carbon emissions was affected more by economic policy than by environmental policy. Finally, we put forward policy suggestions for low-carbon agricultural development in Jilin province.

What drives the decoupling between economic growth and energy-related CO2 emissions in China's agricultural sector?

Many studies have shown that the rapid agricultural mechanization development in China led to substantial energy consumption and CO₂ emission growth. To better explain the mechanism behind the decoupling between economic growth and CO₂ emissions, this paper extends the logarithmic mean Divisia index (LMDI) and production-theoretical decomposition (PDA) considering agricultural decoupling from both structural and technical perspectives. The results reveal that (1) China's agricultural decoupling performance was not ideal. Investment and investment efficiency were the most important factors influencing the decoupling status. The main decoupling obstacle was a higher investment in productivity rather than in energy conservation and carbon reduction. (2) The decoupling status and investment orientation of decoupling efforts among regions were different. Strong negative decoupling statuses frequently occurred in the eastern region, whose main disadvantage was high potential energy intensity. The decoupling status of the central region exhibited expansive features. The decoupling key is to invest more in energy-saving technology rather than in production. The western region changed from weak decoupling to expansive negative decoupling. Both output technology and energy-related factors should be the main investment targets. (3) Weak decoupling and expansive negative decoupling were the most common statuses among provinces. The influence mechanism of drivers exhibited a high spatial heterogeneity at the provincial level. Therefore, the study offered a convincing basis for local governments to formulate low-carbon agricultural development policies by identifying the main decoupling drivers.

How well has economic strategy changed CO2 emissions? Evidence from China's largest emission province

In recent years, Shandong Province became one of China's largest carbon emitters; however, existing studies failed to capture the recent trends and the key driving factors behind it at the city level. In this study, we computed the city-level CO₂ emission by employing accounting methods and Logarithmic Mean Divisia Index (LMDI) to provide a holistic picture and measure the contributing factors CO₂ emissions across 16 cities in Shandong Province during 2010-2018. Research outcomes indicate that Shandong's CO₂ emissions showed an increasing trend during 2010-2018, except in 2013. Shandong Province's GDP per capita and population size promote energy-related CO₂ emissions from 2010 to 2018. Energy intensity is the main driving force behind Shandong's significant CO₂ emission growth, followed by the energy consumption structure. Emission intensity and regional structure partly offset the CO₂ emission increase. Industrial structure is the most important driving factor in reducing emissions; however, its emission reduction effect is not stable in some cities and sectors, especially for the nonmetal and metal industry, petroleum and chemical industry, and energy sector. Dongying is the top emitter across Shandong from 2010 to 2018. Its emissions mainly come from the petroleum and chemical industry. The largest driving factors are the energy intensity and industrial structure. Investigating CO₂ emissions at the city level yields a strong recommendation that Shandong Province's regions cooperate to improve development patterns.

Decomposition and Decoupling Analysis of CO2 Emissions Based on LMDI and Two-Dimensional Decoupling Model in Gansu Province, China

Currently, little attention has been paid to reducing carbon dioxide (CO₂) emissions of Gansu, and the two-dimensional decoupling model has been rarely used to study the relationship between the economic development and CO₂ emissions, especially in western China (e.g., Gansu). Thus, here, we first used the Logarithmic Mean Divisia Index (LMDI) to decompose the driving factors of Gansu's CO₂ emissions between 2000-2017 and then analyzed the decoupling relationship by using the two-dimensional model. Results showed: (1) Gansu's CO₂ emissions increased from 7805.70 × 10⁴ t in 2000 to 19,896.05 × 10⁴ t in 2017. The secondary industry accounted for the largest proportion in Gansu's CO₂ emissions, followed by the tertiary industry and the primary industry. (2) The economic output showed the dominant driving effect on Gansu's CO₂ emissions growth with the cumulative contribution rate of 201.94%, followed by the effects of industrial structure, population size, and energy structure, and their cumulative contribution rates were 9.68%, 7.81%, and 3.05%, respectively. In contrast, the energy intensity effect presented the most obvious mitigating effect with the cumulative contribution rate of -122.49%. (3) The Environmental Kuznets Curve (EKC) between CO₂ emissions and economic growth was demonstrated the inverted U-shape in Gansu. The two-dimensional decoupling status was the low level-weak decoupling (WD-LE) during 2000-2017. Thus, dropping the proportion of the secondary industry, reducing the use of carbon-intensive fuel like coal, introducing advanced technologies, and increasing the investment of new energy might effectively restrain the growth of Gansu's CO₂ emissions.

Driving forces of carbon emissions in China: a provincial analysis

China is the largest carbon dioxide emitter in the world. Over the past decades, China has made great efforts to retard the carbon emission growth. This paper aims to quantify the driving forces of stagewise provincial carbon emission changes and expound the bottom-up provincial efforts aligned with China's carbon abatement targets. During 2007-2012, economic growth as the major contributing factor posed a great increase in provincial carbon emissions, especially in underdeveloped provinces such as Inner Mongolia, Anhui, Guangxi, Shaanxi, and Ningxia. During 2012-2017, the energy intensity effect replaced economic growth as the largest contributor to provincial carbon emission changes. In both two periods, industrial structure upgrading and renewable energy expansion are conducive to mitigate provincial carbon growth, and the power sector will continue to contribute to national decarbonization. Lastly, this paper provides insight into national and provincial carbon emission abatement.

Examining the Driving Factors of Urban Residential Carbon Intensity Using the LMDI Method: Evidence from China's County-Level Cities

Improving carbon efficiency and reducing carbon intensity are effective means of mitigating climate change. Carbon emissions due to urban residential energy consumption have increased significantly; however, there is a lack of research on urban residential carbon intensity. This paper examines the spatiotemporal variation of carbon intensity in the residential sector during 2001-2015, and then identifies the causes of the variation by utilizing the logarithmic mean Divisia index (LMDI) with the help of Microsoft Excel 2016 for 620 county-level cities in 30 Chinese provinces. The results show that high carbon intensity is mainly found in large cities, such as Beijing, Tianjin, and Shanghai. However, these cities showed a downward trend in carbon intensity. In terms of influencing factors, the energy consumption per capita, urban sprawl, and land demand are the three most influential factors in determining the changes in carbon intensity. The effect of energy consumption per capita mainly increases the carbon intensity, and its impact is higher in the municipal districts of provincial capital cities than in other types of cities. Similarly, the urban sprawl effect also promotes increases in carbon intensity, and a higher degree of influence appears in large cities. However, as urban expansion plateaus, the effect of urban sprawl decreases. The land-demand effect reduces the carbon intensity, and the degree of influence of the land-demand effect on carbon intensity is also clearly stronger in big cities. Our findings show that lowering the energy consumption per capita and optimizing the land-use structure are a reasonable direction of efforts, and the effects of differences in influencing factors should be paid more attention to reduce carbon intensity.

Energy efficiency's key role in explaining the performance of energy consumption in Andalusia (Spain)

The EU commitment to improve energy efficiency is conditioned not only by the countries but also by the role that the European regions adopt when they develop their own energy policies. Concretely, due to the economic and energy characteristics of the Andalusia, this region conditions the achievement of Spain's goals in terms of energy efficiency. This paper aims to highlight the key role played by energy efficiency, explaining the energy consumption behaviour in Andalusia and in comparison with the Spanish average for the period 2000-2015. The paper analyses this topic through the Logarithmic Mean Divisia Index (LMDI) decomposition method and with a decoupling index analysis. The results show although the energy efficiency measures have been globally effective in terms of reducing the energy intensity between 2000 and 2015, Andalusia still has a higher energy intensity than the Spanish average and more efforts should be made in order to reduce it and to contribute to Spain's energy consumption targets. The main efforts should be focused on the industry and primary sectors. The energy policy recommendation are two. First, to bring the economic situation of Andalusia closer to the Spanish average and therefore to reduce energy intensity and second, to decouple the energy consumption from economic growth, thus contributing to a reduction in CO₂ emissions.