A multi-sectoral decomposition and decoupling analysis of carbon emissions in Guangdong province, China

Towards a sustainable construction: A newly proposed Tapio-global meta-frontier DEA framework for decoupling China's construction economy from its carbon emissions

Uncovering the decoupling degree and its influencing factors is an important work to look for the carbon abatement and sustainable development in China's construction industry (CCI). To arrive at a solution, we proposed a research framework by taking into account the Tapio decoupling model and biased directional distance function based on global meta-frontier DEA, so as to reveal out the motive force and resistance that can help decouple construction industry economy from its carbon emissions. Discussion results indicate that during the sample period: (1) China's construction economy and carbon emissions exhibited weak decoupling. The advances of energy/production technology were two dominant factors in helping construction economy decoupled from carbon emissions, while construction economic activity exerted the largest effects in impeding decoupling process. (2) The decoupling degree and driving effects differed significantly in three regions. Regional energy/production technology disparity narrowed in central and western region, and further contributed to their decoupling. Energy-biased and output-biased scale change exerted impeding effects on the decoupling in eastern and central region, while exerted diametrically opposite effects on western region's decoupling. Besides, the change of energy/production resource allocation efficiency in eastern and western region acted motivating effects in their decoupling, while formed restriction in central region's decoupling.

Analyzing the spatiotemporal pattern of the decoupling degree between carbon metabolism and economic development in village and town units

In the context of green and sustainable development and rural revitalization, analysis of the relationship between economic development and the evolution of carbon metabolism is of great significance for China's future transformation of development models. This study analyzed the spatial characteristics and spatiotemporal evolution pattern of the decoupling status between carbon metabolism and economic development of Laiwu during two periods from 2001 to 2018 at the village and town unit scales by using the Tapio decoupling model. The results showed that the growth rate of carbon metabolism from 2001 to 2009 was significantly higher than that from 2009 to 2018. The spatial heterogeneity of the decoupling states between economic development and carbon metabolism from 2009 to 2018 was significantly stronger than that from 2001 to 2009 in two units. From 2001 to 2018, the development trend gradually trended towards spatial imbalance. The decoupling status between villages and towns had a high degree of consistency from 2001 to 2009 and inconsistency from 2009 to 2018. From 2001 to 2009, the decoupling status of about 78% of villages was consistent with that of towns. Moreover, from 2009 to 2018, the consistency reduced to 32.2%, and the decoupling status of about 48% of villages was weaker than that of towns. According to the reclassification results of different decoupling state change types, from 2001 to 2018, about 52.2% of the villages had a decoupling state evolution type of eco-deteriorated economic development, which is an unsatisfactory development trend in a short time. Moreover, about 12.1% of the villages had a decoupling state evolution type of eco-improved economic development, which is a satisfactory development trend.

Impact of global value chain embedding on decoupling between China's CO2 emissions and economic growth: Based on Tapio decoupling and structural decomposition

With the increasing fragmentation of global production, China's participation in cross-border production sharing activities has had a considerable impact on the nation's economy and carbon dioxide (CO2) emissions. This study applied the Tapio model to quantitatively evaluate the decoupling between CO2 emissions and economic growth in China, dividing the decoupling index based on global value chains (GVCs) and domestic production within the IO framework, and introducing structural decomposition analysis (SDA) to analyze the GVC-related factors to the decoupling. The relevant research results are fourfold. (1) From 2000 to 2018, China achieved weak decoupling between emissions and economic growth. Domestic and GVC effects each had a negative impact on the decoupling; however, after 2008, the GVC effect had a promotional effect and the negative domestic effect declined. (2) Emission intensity was the primary factor promoting decoupling through domestic and GVC effects, while the scale of final demand was the main hindrance. And the negative effects of GVC-related factors declined following the economic crisis. (3) The regional and sectoral structures of GVC production (58.44 % and 56.08 %) had promotional roles in the changes in GVC effects, while GVC production linkages (-20.19 %) had hindering effects. Various factors contributed to the hindering effect from the 2008 to 2011 index, whereas from the 2011 to 2018 index, all factors contributed to the promotional effect. (4) From 2000 to 2018, the average annual global value chain effect promoted the low-carbon development of China's labor-intensive and knowledge-based manufacturing. In order for GVCs to play a positive role in decoupling, China should promote trade facilitation through international platforms, support the advancement of production technology, reasonably guide China's industries to participate in the regional and industrial links of GVCs, and develop strategic emerging industries.

An interregional environmental assessment framework: revisiting environmental Kuznets curve in China

Achieving the national emission reduction targets requires joint efforts of all jurisdictions, whose sustainable development is affected by complex economic and environmental interactions among regions. An interregional environmental assessment (IREA) framework is constructed for China using multiregional input-output techniques to unravel the carbon emission connections behind interregional economic activities. Then, consumption-based emission accounting is applied in sustainability assessment, in comparison with production-based environmental Kuznets curve (EKC) tests to examine the role of regional connections in shaping EKC. Empirical results expose significant asymmetric CO2 transfer among regions in China, where the Central and Western regions have become CO2 haven for the Eastern region. EKC is valid at the national level and manifests marked regional differences between production- and consumption-based curves. The pollution haven effect alters the EKC curve by expediting the emission peak in the developed Eastern region while delaying it in the developing Western region. Thus, revisiting EKCs in the IREA framework reveals that ignoring interregional connections would lead to misleading results. Only when both production- and consumption-based EKCs transcend their turning points can we claim that environmental governance has ushered in a new era of sustainable development.

Emissions leakage embodied in inter-provincial trade brings unexpected air quality and health benefits

Inter-provincial trade can bring "emission leakage", and consequently influence the air quality and public health. However, there has been a lack of systematic research on air pollution and public health related to emission leakage embodied in inter-provincial trade of China. Here, we systematically evaluated for the first time the influence of emission leakage on national air pollution and related premature deaths in 2012 of China. Unexpected opposite influences of emission leakage on emission and air quality/public health were discovered. Emission leakage embodied in inter-provincial trade in 2012 of China led to an increase of 1.4 % to 4.8 % in national air pollutant emissions, but a decrease of 1.5 % (-0.8 μg·m-3) in population-weighted concentration of PM2.5, while avoiding 1.1 % (-1.4 × 104 people) of premature deaths. Therefore, to reveal the intrinsic mechanism of this opposite influences, we proposed two coefficients, the Concentration per unit primary PM2.5 emission (CPE, unit: μg·m-3/t) and the Death per unit primary PM2.5 emission (DPE, unit: people/t), to characterize the response of air quality and health to emission leakage embodied in inter-provincial trade. Statistical analysis indicated that both the above coefficients showed significant negative correlation (P < 0.05) with provincial PM2.5 emissions changes. The findings offer a means of adjustment and its related evaluation parameters for the emission transfer caused by inter-provincial trade, thereby contributing to further improvement environmental and health benefits through inter-provincial trade.

Decomposition and decoupling analysis of carbon emissions in the Yellow River Basin: evidence from urban agglomerations

A comprehensive understanding of carbon emission reduction and decoupling in urban agglomerations of the Yellow River Basin (YRB) has significant theoretical and practical value for formulating precise carbon reduction policies and achieving ecological conservation and high-quality development in the region. This study utilized a generalized Divisia index decomposition model to identify the primary driving factors behind carbon emission changes in urban agglomerations of the YRB. Based on this, a model measuring decoupling efforts was constructed to systematically investigate the decoupling relationship between carbon emissions. The research findings indicate that technological progress and output scale are two primary drivers of carbon emission increases in the YRB and its urban agglomerations, whereas technological carbon intensity, output carbon intensity, and energy carbon intensity play key roles in reducing carbon emissions. Except for a few years, the YRB and Jiziwan metropolitan area (JWMA) did not exhibit decoupling effects on carbon emissions. The Shandong Peninsula Urban Agglomeration (SPUA) and Central Plains Urban Agglomeration (CPUA) showed strong decoupling effects from 2016 to 2019. The Guanzhong Plain Urban Agglomeration (GPUA) demonstrated a strong decoupling effect from 2013 to 2019 (except from 2016 to 2017). The Lanxi Urban Agglomeration (LXUA) exhibited a strong decoupling effect from 2014 to 2019. Technological carbon intensity plays a decisive role in the transition from non-decoupling to decoupling. Therefore, the government must increase investments in green and low-carbon technologies and strictly implement carbon reduction measures. Thus, the YRB and its urban agglomerations have considerable potential for carbon emission reduction and strong decoupling effects.

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.

Spatial heterogeneity and scenario simulation of carbon budget on provincial scale in China

BACKGROUND

Conducting an extensive study on the spatial heterogeneity of the overall carbon budget and its influencing factors and the decoupling status of carbon emissions from economic development, by undertaking simulation projections under different carbon emission scenarios is crucial for China to achieve its targets to peak carbon emissions by 2030 and to achieve carbon neutrality by 2060. There are large disparities in carbon emissions from energy consumption, the extent of land used for carbon absorption, and the status of decoupling of emissions from economic development, among various regions of China.

RESULTS

Based on night light data and land use data, we investigated carbon budget through model estimation, decoupling analysis, and scenario simulation. The results show that the carbon deficit had a continuous upward trend from 2000 to 2018, and there was a significant positive spatial correlation. The overall status of decoupling first improved and then deteriorated. Altogether, energy consumption intensity, population density of built-up land, and built-up land area influenced the decoupling of carbon emissions from economic development. There are significant scenarios of carbon emissions from energy consumption for the study area during the forecast period, only in the low-carbon scenario will the study area reach the expected carbon emissions peak ahead of schedule in 2027; the peak carbon emissions will be 6479.27 million tons.

CONCLUSIONS

China's provincial-scale carbon emissions show a positive correlation with economic development within the study period. It is necessary to optimize the economic structure, transforming the economic development mode, and formulating policies to control the expansion of built-up land. Efforts must be made to improve technology and promote industrial restructuring, to effectively reduce energy consumption intensity.

Energy-water nexus in low-carbon electric power systems: A simulation-based inexact optimization model

Energy and water resources are closely linked in electric power systems, and the application of low-carbon technologies further affects electricity generation and water consumption in those systems. The holistic optimization of electric power systems, including generation and decarbonization processes, is necessary. Few studies have considered the uncertainty associated with the application of low-carbon technologies in electric power systems optimization from an energy-water nexus perspective. To fill such a gap, this study developed a simulation-based low-carbon energy structure optimization model to address the uncertainty in power systems with low-carbon technologies and generate electricity generation plans. Specifically, LMDI, STIRPAT and grey model were integrated to simulate the carbon emissions from the electric power systems under different socio-economic development levels. Furthermore, a copula-based chance-constrained interval mixed-integer programming model was proposed to quantify the energy-water nexus as the joint violation risk and generate risk-based low-carbon generation schemes. The model was applied to support the management of electric power systems in the Pearl River Delta of China. Results indicate that, the optimized plans could mitigate CO₂ emission by up to 37.93% over 15 years. Under all scenarios, more low-carbon power conversion facilities would be established. The application of carbon capture and storage would increase energy and water consumption by up to [0.24, 7.35] × 10⁶ tce and [0.16, 1.12] × 10⁸ m³, respectively. The optimization of the energy structure based on energy-water joint violation risk could reduce the water utilization rate and the carbon emission rate by up to 0.38 m³/10⁴ kWh and 0.04 ton-CO₂/10⁴ kWh, respectively.

A Leading Role of Water Resources and Animal Husbandry in Environmental Sustainability: A Case Study of China

Animal husbandry is an important emission source of greenhouse gas. In order to discover the real situation of carbon emission in China's animal husbandry scientifically, the paper measured and calculated carbon emission in China's animal husbandry from 1997 to 2017 on the basis of soil and water resources. In addition, analyzing its time-order characters, structural characters, driving factors and decoupling relationships are all done in this treatise. Major findings are as follows: (1) The carbon emission of China's animal husbandry in 2017 was 374.3528 million tons, an increase of 17.8066 million tons over 1997, with the average annual growth rate of 0.24% and the average annual carbon emission of 398.7817 million tons; (2) There was a decreasing trend in carbon emission of intestinal fermentation in China's animal husbandry while there was an increasing trend in carbon emission of manure emission in China's animal husbandry; (3) The carbon emission of China's animal husbandry peaked in 2006 and went through three phases of up-down-steady between 1997 and 2017; (4) The contribution of cattle, pig, sheep, other large livestock, poultry and rabbits to China's animal husbandry carbon emissions decreased in turn, and the average contribution of cattle, pigs and sheep to China's animal husbandry carbon emissions was as high as 98.15%. (5) Five factors reducing carbon emission of China's animal husbandry were carbon intensity, agricultural industrial structure, agricultural population-water resources matching degree, agricultural water-soil resources and per capita cultivated land area. Two factors increasing carbon emission of China's animal husbandry were population and economic benefits of agriculture per unit agricultural population; (6) There was a generally weak decoupling between carbon emission in China's animal husbandry and animal husbandry's economic growth from 1997 to 2017.

Decoupling relationship between carbon emissions and economic development and prediction of carbon emissions in Henan Province: based on Tapio method and STIRPAT model

In order to cope with global warming, China has put forward the "30 · 60" plan. We take Henan Province as an example to explore the accessibility of the plan. Tapio decoupling model is used to discuss the relationship between carbon emissions and economy in Henan Province. The influence factors of carbon emissions in Henan Province were studied by using STIRPAT extended model and ridge regression method, and the carbon emission prediction equation was obtained. On this basis, the standard development scenario, low-carbon development scenario, and high-speed development scenario are set according to the economic development model to analyze and predict the carbon emissions of Henan Province from 2020 to 2040. The results show that energy intensity effect and energy structure effect can promote the optimization of the relationship between economy and carbon emissions in Henan Province. Energy structure and carbon emission intensity have a significant negative impact on carbon emissions, while industrial structure has a significant positive impact on carbon emissions. Henan Province can achieve the "carbon peak" goal by 2030 years under the standard and low-carbon development scenario, but it cannot achieve this goal under the high-speed development scenario. Therefore, in order to achieve the goals of "carbon peaking" and "carbon neutralization" as scheduled, Henan Province must adjust its industrial structure, optimize its energy consumption structure, improve energy efficiency, and reduce energy intensity.

Towards low-carbon development: The role of industrial robots in decarbonization in Chinese cities

Technological advancements have played a key role in improving energy efficiency and reducing emissions, and industrial robots are important carriers of intelligent manufacturing and industrial upgrading. Although various countries and regions are under pressure to reduce their carbon emissions, a consensus has not been reached on whether industrial robots can help. This study investigates how industrial robots affect carbon emissions by categorizing industry data from the International Federation of Robotics (IFR, 2010-2018) into city-level variables. The empirical finding revealed that cities' carbon emissions have been significantly reduced by the application of industrial robots. By using the penetration of robots in Chinese cities as an instrumental variable constructed through the combination of employment level and robot imports, the beneficial role of robots is further verified by a plausibly exogenous test. The mechanism analysis revealed that industrial robots contribute to cities' decarbonization by enhancing energy efficiency and green technology efficiency. The heterogeneity analysis showed that the effect of industrial robots on decarbonization is more pronounced in megacities, advanced manufacturing bases, and low-carbon pilot cities. This study empirically confirms the positive role of industrial robots in carbon emission reduction, provides evidence for industrial robots' technical characteristics of decarbonization, and proposes novel ideas for achieving net-zero carbon emissions.

Identifying carbon emission characteristics and carbon peak in China based on the perspective of regional clusters

Global warming has endangered the natural ecosystem's balance, as well as human existence and development, and it is mostly caused by carbon dioxide. Identifying carbon emission characteristics and predicting carbon emission reasonably is helpful to provide indication for the effective design of emission reduction path. The most literature use a single prediction model; this paper predicts carbon emission using a number of strategies based on previous research. Considering the prediction accuracy, advantages, and disadvantages of each method, a new method combining autoregressive integrated moving average (ARIMA) model and NAR neural network (NAR-NN) is proposed; in addition, this paper attempts to explain the carbon emission characteristics and emission reduction paths of each region from the new perspective of clustering. First, the results show that China's carbon emission features can be divided into four categories: low-carbon demonstrative type, low-carbon potential type, high-carbon developed type, and high-carbon traditional type. Moreover, low-carbon demonstrative type includes merely Beijing and Shanghai, low-carbon potential type is distributed in the southeast coastal areas of China, the high-carbon developed type is mainly distributed in Northeast China, and the western region basically belongs to high-carbon traditional type. Second, ARIMA model and NAR-NN are the two best methods in terms of prediction effect, and the combined model has better prediction effect than the single model. Third, carbon emissions in most regions of China will increase in the next few years; the time of carbon peak in the east is earlier than that in the west regions of China. Beijing will probably be the first region in China to complete the carbon peak. Besides, there is a certain correlation between the carbon peak time and the type of carbon emission in each region.

Low-carbon transformation planning of China's power energy system under the goal of carbon neutrality

China has become the largest energy producer and consumer in the world. Its carbon emissions account for 80% of its total carbon emissions, while the carbon emissions caused by energy consumption in the power industry account for more than 50%. To ensure that the 2030 carbon-peak and 2060 carbon-neutral targets are achieved, it is imperative to carry out low-carbon energy transformation in the power industry. The paper compares and analyzes the technical level of six high-energy-consuming industries: power, steel, cement, aluminum smelting, petrochemical industry, and coal chemical industry in terms of low carbon. The results show that the structural adjustment of China's high-energy-consuming industries has reached the upper limit, and the low-carbon transformation of power and energy has become inevitable. The carbon emissions of China's six regional power grids are statistically analyzed. The background of the power generation proportion of China's thermal power, hydropower, nuclear power, wind power, solar power and other different energy systems from 2018 to 2020 is analyzed, and the development trend is predicted. The low-carbon emission path of power energy is proposed. Based on the EnergyPLAN model, the power energy structure of carbon peaking in different scenarios from 2020 to 2030 is constructed, and the power energy system's carbon dioxide emission reduction paths under different scenarios are obtained. The sustainability impact of different power generation combination scenarios is comprehensively evaluated using the multi-index evaluation method, and the optimal path of the power system to energy scenario is selected. The research conclusion provides a basis for the power sector's renewable energy power generation path selection.

Decomposing manufacturing CO2 emission changes: An improved production-theoretical decomposition analysis based on industrial linkage theory

Identifying key drivers of manufacturing CO₂ emissions is critical to carbon reduction practices. For manufacturing, CO₂ emissions are mainly determined by production capacity and production scale. However, traditional production-theoretical decomposition analysis (PDA) fails to consider production-scale-related drivers. To better support policy development and implementation, this paper improves PDA based on industrial linkage theory. The improved model can identify seven production-capacity-related drivers and five production-scale-related drivers, allowing a comprehensive understanding of CO₂ emission drivers. Then this model is implemented to investigate CO₂ emission changes in 18 manufacturing sectors in Hubei Province, China, from 2012 to 2017. Results show that manufacturing CO₂ reduction efforts in Hubei Province have yielded some achievements, with reduced potential energy intensity and improved CO₂ emission technical efficiency in most sectors. Changes in external market demand and final demand structure have contributed to CO₂ reduction in most sectors. Results also reveal some problems in manufacturing in Hubei Province, such as the inability to improve CO₂ emission technical efficiency and CO₂ emission technology strength, the slow improvement of energy utilization technical efficiency and energy utilization technology strength, and the reduction of value-added rate.

Exploration of CO2 emission reduction pathways: identification of influencing factors of CO2 emission and CO2 emission reduction potential of power industry

Low-carbon development of China's power sector is the key to achieving carbon peaking and carbon neutrality goals. Based on the logarithmic mean divisor index (LMDI) model, considering the carbon transfer caused by inter-provincial electricity trading, this paper analyzes the influencing factors of CO2 emissions in the provincial power sector and uses K-means clustering method to divide 30 provinces into four categories to analyze the differences in regional carbon emission characteristics. In addition, by establishing different development scenarios, the carbon emission trends and emission reduction potentials of each cluster under different emission reduction measures from 2020 to 2040 are studied, in order to explore the differentiated emission reduction paths of each cluster. The results show that the contribution of influencing factors shows great differences in different provinces. Trends in CO2 emissions vary widely across scenarios. In the reference scenario, the CO2 emissions of each cluster will continue to increase; in the existing policy scenario, the total power industry will peak at 6.1Gt in 2030; in the advance peak scenario that puts more emphasis on the development of advanced technologies and renewable energy under the clean development model, the carbon emission peak will be brought forward to 2025, and the peak will be reduced to 5.2Gt. Finally, differentiated emission reduction paths and measures are proposed for the future low-carbon development of different cluster power industries, providing theoretical reference for the deployment of provincial-level emission reduction work, which is of great significance to the global green and low-carbon transformation.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s10098-022-02456-1.

Exploring the embodied carbon flow interactive relationships in China from an ecological network perspective: a model framework and application at provincial level

Energy-related carbon emissions take a large proportion in China, and the interregional trade caused by provincial disparities has led to significant differences in carbon footprint (CF) and embodied carbon flows among provinces that make great bottlenecks for the balance of economic development and carbon mitigation. In this study, we developed an embodied carbon flow-based ecological network (ECFEN) model with economic trade and emission data through combining the multi-regional input-output model and ecological network analysis approach. The developed ECFEN model was applied to 30 provinces in China to quantify the interprovincial flow of carbon embodied in final goods and services and identify the ecological utility (competitive, exploitative, and exploited) and control/dependence relationships between different regions. The main findings can be summarized as follows: Firstly, Jiangsu had the highest total CF with amount of 906 Mt, which was approximately 24.5 times than that of Hainan (37 Mt). Especially, the local CF in Shandong was the largest among all of the provinces with an amount of 683 Mt. Secondly, 13 pairs of embodied carbon flow paths exceeded 20 Mt, and the remarkable embodied carbon flowed from resource-oriented regions (e.g., Inner Mongolia, Shanxi, Hebei) to economically developed eastern coastal provinces and municipalities (e.g., Jiangsu, Guangdong, Beijing, Chongqing). Metallurgy and electricity, water, and gas contributed 30-80% of the total embodied carbon import and export for each province. Thirdly, the exploitative and exploited relationship dominated the ecological relationship between provinces. Meanwhile, the resource-oriented regions played the role of controllers while economically developed provinces were dependents. On the one hand, the central government could take vertical compensation measures such as financial subsidies for major exporter and controllers. On the other hand, it is necessary to take horizontal technical transfer especially from economically developed southeast coastal provinces to western underdeveloped inland area. The obtained results and policy implications are expected to provide reasonable insights for decision-makers to formulate carbon mitigation strategies under the domestic circulation strategy.

Water-carbon nexus relationship and interaction mechanism analysis within Beijing-Tianjin-Hebei urban agglomeration

As the total water resources consumption control and carbon mitigation continuous improvement, the weak water-carbon incorporate management is increasingly exposed. In this study, a water-carbon nexus assessment framework is proposed to analyze the nexus relationship between water consumption and carbon emission, and distinguishes the coupled water-carbon transmission intensity and the transfer paths under regional and industrial scales. According to the practical input-output table, water consumption, and carbon emission information, the framework is applied to Beijing-Tianjin-Hebei urban agglomeration (BTHUA), a population, resource, and trade intensive area of China. Inter-regional/intra-regional water consumption and carbon emission transfer fluxes between sectors, the pairwise ecological relationship, and the water-carbon nexus were analyzed. Results indicated that the water-carbon transfer indexes from Hebei to Beijing and Tianjin were 161.85 kg/m³ and 113.88 kg/m³ in the study period, along with the most water consumption and carbon emission, and the worst water-carbon nexus. From the industrial perspective, electricity and gas supplying industry provided 7.8% and 29.1% of the total carbon transfer in Tianjin and Hebei, as the most key node sectors on the water-carbon nexus in the BTHUA. The research provides valuably supporting the adjustment of the existing urban agglomeration water-carbon nexus management schemes.

Analysis of the decoupling state and driving forces of China's construction industry under the carbon neutrality target

The essential to achieving the 2060 carbon neutrality target in China lies in the performance of the construction industry. Decoupling economic development from CO₂ emissions is the main strategy for reducing emissions in the construction industry. This paper is based on panel data for China and its 30 provinces during 2009-2019. A Tapio decoupling model is constructed to analyze the decoupling state of economic development and CO₂ emissions in the construction industry. The logarithmic mean Divisia index model is constructed to continue the decomposition of the drivers of the decoupling state and CO₂ emissions. The results show that (1) the economic development level of most provinces is positively correlated with their CO₂ emissions; (2) Beijing and Jiangsu reach the ideal strong decoupling state, and Heilongjiang has the worst decoupling state. The same type of decoupling state shows a certain aggregation phenomenon in space; (3) economic output plays a critical role in promoting CO₂ emissions and decoupling of the construction industry in China and the provinces. The main driver of decoupling is indirect carbon intensity; (4) energy intensity has a greater impact on CO₂ emissions reduction in regions with more developed economic levels. Understanding the drivers of the decoupling state in China's construction industry provides a valuable basis for energy efficiency and emission reduction efforts in China and other countries.

Towards low-carbon domestic circulation: Insights from the spatiotemporal variations and socioeconomic determinants of emissions embedded within cross-province trade in China

For a country like China with unbalanced development pattern among provinces, domestic circulation (i.e., cross-province trade) is important for the long-term stability and prosperous development of economic market. However, with the rapid advance of integration of domestic regional economy, while expanding the internal market scale and deepening the provincial division of labor network for promoting the economic growth, the carbon emissions embedded within the cross-province traded products and services cannot be underestimated. Under the background of climate-trade dilemma, it is necessary to exploring the spatiotemporal variations and socioeconomic determinants of provincial "invisible" carbon emissions for a better understanding of trade-induced eco-environmental effects. To that end, this study developed an environmental-economic system model through integrating the environmentally extended multiregional input-output method and weighted average structural decomposition analysis technique to explore the trade-related emissions at the provincial level and generate the mitigation-management strategies for decisionmakers. Overall, more than half the emissions were embedded within cross-province goods and services trade over the whole study period. Furthermore, the distribution of traded emissions showed obvious spatial heterogeneity and great unbalance was existed between provincial imports and exports. Among all provinces, carbon surplus provinces were always more than deficit ones and the trading patterns of approximately 65% regions remained unchanged during 2007-2017. Remarkably, the emissions trading pattern undergone transition from carbon deficit to carbon surplus in provinces like Henan, Hubei, Guizhou, and so on. Conversely, provinces like Jilin, Shanghai, and Xinjiang showed opposite change. With the prevalence of online payment and electronic commerce in the future, the central and sub-national government could consider launching a pilot project for the design and creation of personal carbon consumption account in the carbon surplus provinces such as Guangdong, Henan, and Jiangsu. Meanwhile, for the provinces with larger carbon exports, it is necessary to establish the horizontal high technical transfer channels and vertical compensation mechanisms such as financial subsidies for improving the low-carbon production level. Our findings provided a holistic depict of national traded emissions at the provincial level, highlighting the importance of cross-province emission effect in exploring ways to promote the low-carbon transition of domestic circulation and fulfill the high-quality development of 'dual circulation' new pattern and successful achievement of 'double carbon' solemn commitment.

A Cooperative-Dominated Model of Conservation Tillage to Mitigate Soil Degradation on Cultivated Land and Its Effectiveness Evaluation

Sustainable agricultural production systems are important for ensuring food security. However, they are severely threatened by soil degradation and carbon emissions resulting from traditional farming practices. A cooperative-dominated conservation tillage model attempts to mitigate these issues, yet it is not clear how this model has been implemented and how well it performs in practice. This study takes Lishu County in Jilin Province in Northeast China as a case study to explore the implementation of a cooperative-dominated conservation tillage (CDCT) model and its practical effectiveness. In contrast to the traditional production model, this model uses cooperatives as the direct managers of cultivated land and promotes the construction of new production units and large-scale and mechanized operations to standardize the application of conservation tillage technology in agricultural production. Scientific research institutes, governments, and enterprises are supporters of cooperatives, empowering them in terms of technology, capital, products, and services. The evaluation results show that, unlike the traditional production model, which caused a decrease in the soil organic carbon content, the organic carbon content of the topsoil of cultivated land under this model increased by an average of 6.17% after 9 years of conservation tillage application. Furthermore, the soil structural stability index of the cultivated land increased from 3.35% to 3.69%, indicating that the degree of soil structural degradation was alleviated to a certain extent. The CDCT model effectively enhanced the operational efficiency and fertilizer use efficiency, and the carbon footprint of maize production was also reduced by 15.65% compared to the traditional production model. In addition, the total production cost was reduced by 1449 CNY/ha and profit increased by 2599 CNY/ha on average, indicating higher economic returns under the CDCT model due to increased yields and lower input costs. Farmers who are freed from agricultural production activities by transferring their farmland can also gain two types of income—land revenue and labor wagesi—thus mproving their living conditions. The CDCT model can deliver multigoal benefits and be of great value in its extension to other regions. This study may provide lessons for the sustainable use of cultivated land in China and other developing countries, contributing to agricultural development with lower environmental costs.

Multiple accounting and driving factors of water resources use: A case study of Shanghai

Previous research papers on urban water resources accounting were confined to the perspectives of production and consumption, ignoring the perspective of income. This paper proposes a systems framework to analyze the income, production, and consumption-based water uses and underlying driving forces of a city based on the methods of multi-scale input-output analysis and structural decomposition analysis. A case study is performed for Shanghai as a megacity. The results show that the income, production and consumption-based water uses of Shanghai had decreased from 5.70 billion m³, 10.85 billion m³ and 28.45 billion m³ in 2007 to 2.80 billion m³, 6.20 billion m³ and 24.10 billion m³ in 2017, respectively. Domestic imported primary inputs had emerged as an important virtual water supplier of Shanghai and its share of total supply-side water use had increased from 23.92% in 2007 to 42.95% in 2017. Meanwhile, about 46% and 40% of Shanghai's total consumption-based water use had been imported from other Chinese regions and foreign countries in 2017, respectively. It is revealed that trade played an important role in relieving water use pressure in Shanghai. The factors that had increased the uses of water resources in Shanghai include population, per capita value-added, per capita output, final consumption structure, and per capita final consumption. The factors that had reduced the water uses in Shanghai include technology, value added mix, output structure, value added structure, domestic import, commodity mix, and foreign import. It is suggested that in addition to curbing urban water use from the production side, more targeted water-saving measures should be devised from the supply (e.g., restricting loan to heavy water-consuming enterprises) and consumption sides (e.g., encouraging residents to buy low-water products).

Decomposition and prediction of China's carbon emission intensity towards carbon neutrality: From perspectives of national, regional and sectoral level

China has actively participated in global climate governance and put forward its ambitious carbon neutrality target. The formulation of scientific plans has become the primary concern of the policy makers, especially for the 14th and 15th Five-Year Plans (FYP) which are important periods to secure the neutrality pledge and transform the whole economy. Since the carbon emission intensity play a key role in achieving carbon neutrality, it is necessary to summarize and explore the evolution trend of carbon emission intensity as well as its driving factors. Therefore, an integrated decomposition framework is developed to study the carbon emission intensity in the past three FYPs from the national, regional and industrial levels. Furthermore, towards the carbon neutrality target, moderate scenario and advanced scenario are designed to predict the future evolution trend of the carbon emission intensity and driving factors in the 14th and 15th FYPs (2021-2030). The main results are as follows: (1) During the three FYPs, factor substitution is the main force contributing to the decreased carbon emission intensity, but this effect gradually decreased. This indicates that it is an inevitable trend to further promote internal optimization and reform of energy system. (2) The change of energy structure exerts a positive effect on the carbon emission intensity decline, but it is not significant, especially in the industrial sector. (3) With the rich factor endowment, central and eastern regions can reduce carbon emission intensity through factor substitution and industrial structure transformation, while the western region is not. (4) In the future, the role of industrial structure optimization and technology progress will be gradually significant. Finally, our findings provide practical guidance on achieving carbon emission intensity reduction and enlightenments on policymaking.

Dynamic Evolution and Regional Disparity in Carbon Emission Intensity in China

China’s carbon reductions are of great significance to the realization of global temperature control targets. Carbon emission intensity (CEI) represents the degree of coordination between emissions and economic development to some extent. Nevertheless, there is a paucity of research on its spatial–temporal evolution and regional differences. To fill the gap, this study exploits the Theil index to shed light on the characteristics of its spatial–temporal distribution and regional disparities in China during the period of 2000–2019, and constructs a multi-regional spatial index decomposition model to analyze the differences in its drivers. The results indicate that the decreasing CEI during the period of 2000–2019 shows a distinctive imbalance in spatial–temporal distribution. The gap between north and south is greater than that between east and west. The expansion of the Theil index based on CEI reveals a widening tendency of the mismatch between emissions and economic development among provinces. CEI disparity is mainly due to growing intraregional differences. For most provinces, the energy intensity effect is the essential driver of spatial differences regarding CEI, with the energy structure and the industrial structure effects gradually changing from promoting to inhibiting effects. The carbon emission factor effect has no significant fluctuation, but regional differences are distinct.

A Calculation and Decomposition Method Embedding Sectoral Energy Structure for Embodied Carbon: A Case Study of China’s 28 Sectors

The measurement and allocation of carbon emission responsibilities is a fundamental issue in China’s low-carbon development. However, existing studies of embodied carbon do not sufficiently consider the sectoral energy structure. In this work, we developed a high-resolution calculation method for embodied carbon that embeds the sectoral energy structure into traditional input–output methods, thus expanding the driving factors of SDA decomposition. Based on this method, we calculated the quantity, final consumption structure, and energy structure of embodied carbon in China’s 28 sectors from 2002 to 2018, drew a carbon emissions allocation Sankey diagram of China in 2018, and calculated the SDA decomposition results for 2002–2010 and 2010–2018. The results indicate that fixed capital formation was still the top contributor of embodied carbon, and it caused more coal consumption. “Construction for fixed capital formation” and “other services for domestic consumption” were the two most important drivers of carbon emissions. The final consumption quantity and energy intensity were the main factors that promoted and inhibited the growth of embodied carbon, respectively, while the effects of the input–output structure, sectoral energy structure, and carbon emission coefficient on reducing carbon emissions were obvious after 2010. This also revealed that policymakers should formulate differentiated emission reduction strategies according to the carbon emission characteristics of key sectors.

Key Factors Influencing the Achievement of Climate Neutrality Targets in the Manufacturing Industry: LMDI Decomposition Analysis

The manufacturing industry is often caught in the sustainability dilemma between economic growth targets and climate action plans. In this study, a Log-Mean Divisia Index (LMDI) decomposition analysis is applied to investigate how the amount of industrial energy-related CO2 emissions in Latvia has changed in the period from 1995 to 2019. The change in aggregate energy-related CO2 emissions in manufacturing industries is measured by five different factors: the industrial activity effect, structural change effect, energy intensity effect, fuel mix effect, and emission intensity effect. The decomposition analysis results showed that while there has been significant improvement in energy efficiency and decarbonization measures in industry, in recent years, the impact of the improvements has been largely offset by increased industrial activity in energy-intensive sectors such as wood processing and non-metallic mineral production. The results show that energy efficiency measures in industry contribute most to reducing carbon emissions. In the future, additional policies are needed to accelerate the deployment of clean energy and energy efficiency technologies.

Exploring the Dependence and Influencing Factors of Carbon Emissions from the Perspective of Population Development

Working towards sustainable population development is an important part of carbon mitigation efforts, and decoupling carbon emissions from population development has great significance for carbon mitigation. Based on the construction of a comprehensive population development index (PDI), this study adopts a decoupling model to explore the dependence between carbon emissions and PDI across 30 Chinese provinces from 2001 to 2017. Then, the stochastic impacts by regression on population, affluence and technology (STIRPAT) model is used to investigate the impact of population factors on carbon emissions. The results show that the decoupling relationship between carbon emissions and PDI has experienced a transformation from expansive negative coupling to expansive coupling and then to weak decoupling at the national level, while some provinces have experienced the same evolutionary process, but the decoupling state in most provinces is not ideal. Sending talent to western provinces and developing low-carbon supporting industries will accelerate carbon decoupling. At the national level, incorporating environmental protection into the existing education system as part of classroom teaching could contribute to carbon decoupling.