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[Display omitted] • Measure carbon emission inequalities (CEIs) of urban agglomerations under rapid urbanization. • Identify the key factors determining CEIs within the three urban agglomerations. • Account for the differences between adjacent or distant urban agglomerations. • The declining CEI is mainly due to changes in industrial structure and economic development. • CEI in the upper reaches of the YREB increases due to the changes in energy intensity. Increasing countries are articulating ambitious goals of carbon neutrality. However, large inequalities in regional emissions within a country may hinder progress toward a carbon–neutral future, as the unequal distribution of reduction responsibilities among regions could impair just transition and exacerbate uneven development, which necessitates an in-depth understanding of the mechanism of multi-scale carbon inequalities within country, region, and city. Yet, the evolution of carbon inequalities within urban agglomerations and the differences between adjacent or distant urban agglomerations have not been well understood, especially in countries undergoing rapid urbanization. Using the data of 89 cities in China's Yangtze River Economic Belt (YREB) during 2006–2021, this paper quantifies carbon emissions inequality (CEI) at different scales in a systematic regional-urban agglomeration-city hierarchical structure. Then, under the integrated mean logarithmic deviation-logarithmic mean Divisia index (MLD-LMDI) decomposition framework, multi-scale CEIs are perfectly decomposed into six interrelated drivers, i.e., industrial emission structure, energy emission intensity, industrial energy mix, energy intensity, industrial structure, and economic development. The results show that economic development, energy intensity, and industrial energy mix disparities are the main determinants accounting for CEIs at different scales. The decreasing CEI in YREB is mainly due to the changes in industrial structure and economic development, while the energy intensity effect partially hinders the mitigation of CEI. In the upper reaches of the YREB, the energy intensity effect accounts for over 94% growth of CEI during 2006–2021, while the decline in CEIs in middle and lower reaches is primarily caused by the effects of industrial energy mix and industrial structure, respectively. Further spatial decomposition analysis reveals more refined city-level heterogeneous effects and emphasizes the prioritized emission reduction direction for each city. This paper offers implications for reducing carbon inequality and insights into coordinated carbon emissions mitigation at the regional level for a carbon–neutral future. [ABSTRACT FROM AUTHOR]

• ·The three-stage super efficiency SBM-DEA model and Malmquist index was used to measure carbon emission efficiency, index and decomposition terms of land use. • ·LUCE and its spatial and temporal pattern of urban agglomerations in the YREB were discussed from static and dynamic perspectives respectively. • ·Environmental variables such as level of economic development, urbanization rate, and industrial structure all have significant effects on input slack value. • ·The PTE of the MRYR was relatively low due to insufficient resources in low-carbon technology, while the low TE of the CC is due to its insufficient SE. • ·TC was the main factor promoting the improvement of LUCE in the whole basin, and the trend changes of LUCE in the three urban agglomerations was closely related to their management level, technological level, and adjustment of element structure. Exploring regional differences in land use carbon emission efficiency (LUCE) and the path of collaborative emission reduction in the Yangtze River Economic Belt (YREB) are of immense importance to achieve the sustainable regional development. Firstly, based on the estimation method of land use carbon emissions, this paper scientifically calculates the carbon emission of land use of the Yangtze River Delta urban agglomeration (YRD), the middle reaches of the Yangtze River urban agglomeration (MRYR), and the Chengdu-Chongqing urban agglomeration (CC) from 2010 to 2021 in the YREB. And the carbon emission of land use of each city in urban agglomerations was obtained, which was regarded as the unexpected output in the efficiency evaluation index system. Secondly, the three-stage SBM-DEA model was used to reveal the LUCE and its spatiotemporal characteristics in different regions from a static perspective. The empirical results of the second stage SFA showed that the level of economic development, urbanization rate, industrial structure, non-agricultural conversion rate of agricultural land, the level of scientific and technological innovation and government intervention had significant effects on LUCE. By comparing the efficiency values and their decompositions in the first and third stage of various urban agglomerations, it was found that after eliminating environmental and random factors, the average ranking of comprehensive technical efficiency (TE) of land use carbon emission changed from YRD > MRYR > CC to YRD > CC > MRYR due to the cross influence of pure technical efficiency (PTE) and scale efficiency (SE). Thirdly, Malmquist index model was used to analyze the change of total factor productivity index (TFPch) and its spatiotemporal pattern from a dynamic perspective. It was found that the TFPch, technological progress index (TC), pure technical efficiency index (PEC) and scale efficiency index (SEC) of land use carbon emission of the three major urban agglomerations all showed varying degrees of increase after adjustment. Finally, to further analyze the impact of technological progress, PTE and SE on LUCE, a regression model was constructed with LUCE of each city in urban agglomeration as the dependent variable, and TC, PEC and SEC as independent variables. It was concluded that technological progress was the main factor to promote the improvement of LUCE in the whole YREB. The trend of changes of LUCE in various urban agglomerations was closely related to its management level, technology level and factor structure adjustment. The above conclusions provide support for the coordinated emission reduction path of urban agglomerations in the YREB from the perspective of land use, which is conducive to actively and steadily promoting the realization of carbon peak and carbon neutrality goals. [ABSTRACT FROM AUTHOR]

• Investigation of GHG pollutants persistence in Europe and major economic areas. • Mean reversion properties and the integration factor is examined for CO2, CH4, and NO2 (2000-2021) • Empirical evidence of mean reversion in CO2 emissions across all countries. • CH4 and NO2 show reduced persistence and not all countries exhibit mean reversion properties. • These empirical findings highlight policy progress, but underscore the need for further efforts. This paper investigates the persistence of GHG in Europe, evaluating the effectiveness of government policies, including the Kyoto Protocol and the EU Council commitment. Mean reversion properties, and the structure of the integration factor were examined to determine the degree of persistence across markets and assess policy effectiveness. The analysis focuses on CO 2 , CH 4 , and NO 2 pollutants, with monthly data starting in January 2000 and ending in December 2021 from major European countries, and the US, Japan, Brazil, China, and India for comparison purposes. Empirical results show clear evidence of mean reversion in CO 2 emissions observed across all countries, indicating certain degrees of stabilization. Similar patterns are seen for CH 4 , and NO 2 , observing reduced persistence of these pollutants; however not all countries exhibit mean reversion properties. Thus, these findings highlight policy progress in stabilizing GHG emissions, particularly for CO 2 , but underscore the need for further efforts to achieve a substantial emissions reduction. [ABSTRACT FROM AUTHOR]

• China's sustainable development relies on agricultural trade and carbon emissions. • Carbon emission transfer index in agricultural trade is measured. • Decoupling-recoupling model and integrated prediction model are constructed. • A unified framework for trade, environment and technology is established. Under the new development pattern of domestic and international "dual circulation" in China, studying the relationship among green technology innovation, agricultural trade deficit (ATD) and carbon emission transfer embodied in agricultural trade (ATCT) becomes crucial for achieving coordinated and unified high-quality economic development and efficient environmental protection. Based on this, the paper uses environmental input–output model, "two-stage" decoupling and recoupling model, panel vector auto-regressive model, spatial econometric model and "rolling" Grey-BP neural network prediction model to explore present status of ATD, ATCT and their relationship, driving role of green technology innovation, as well as whether that relationship will be improving in the future. The results reveal the following: (1) China's ATD and ATCT show an increasing trend year by year, and their inter-provincial differences are gradually significant over time. The overall relationship between ATD and ATCT does not display ideally, which makes it necessary to quickly transform that relationship into reasonable and ideal state. (2) Green technology innovation has significant positive "U-shaped" and inverted "U-shaped" local effects on ATD and ATCT respectively, with a reasonable range of ATD reduction while ATCT increase valuing from 0 to 0.290, and has significant positive "U-shaped" spatial spillover effect on both without any reasonable range. The short-term effect of green technology innovation on coordinated promotion between ATD and ATCT is superior to the long-term one. (3) Green technology innovation has a significant regional heterogeneity. Regional differences must be taken into consideration seriously, and the measures adapted to local conditions are crucial. The role of fundamental and progressive green technology innovation has heterogeneity, which means that targeted development and utilization of green technology products are important. (4) Regardless of the time horizon, the relationship between ATD and ATCT will improve, which can provide a key step for coordinated development of economy and environment in agricultural trade. Therefore, in order for China to implement and promote further opening-up, reduce agricultural trade dependence, ensure food security and important agricultural product supply, as well as achieve the unity of high-quality economic development and ecological efficient protection, it is necessary to reasonably reverse trade deficit and increase carbon emission transfer in agriculture. [ABSTRACT FROM AUTHOR]

• Carbon emissions from energy consumption of 282 prefecture-level cities in China were calculated. • A new method for calculating carbon emission factors for electricity is proposed. • Extending the Atkinson Index to the application of Carbon Fairness. • Measured the inequality and fairness indicators of carbon emissions from energy consumption at the prefecture-level city level in China. This paper focuses on the conceptualization and quantification of carbon emission inequality and fairness from energy consumption. In order to more accurately measure the carbon emissions generated by electricity consumption in each city, we firstly propose a new method for calculating the carbon emission factor for electricity, and calculates the carbon emissions from energy consumption in 282 prefecture-level cities in China from 2003 to 2019. Through the application of the Theil-Kaya decomposition mechanism and the improved Atkinson index, we conducted an in-depth study of the carbon emission inequality and fairness indicators from energy consumption. The results show that (1) Although China's overall inequality has declined, it remains significant in the western region, with its inequality indicator fluctuating from 0.548 in 2003 to 0.683 in 2019.(2) The decomposition of the drivers of inequality indicators shows that the contribution of GDP per capita to inequality is increasingly being replaced by energy intensity and the energy carbon emission factor, with energy intensity emerging as the main driver, contributing more than 40% to inequality in 2019, both overall and in the three regions.(3) The results for the fairness indicator, on the other hand, show a certain degree of divergence. Further Dynamic Time Warping fitting analysis shows that regardless of how the public's sensitivity to carbon emissions varies, the change trends of the fairness and inequality indicators for carbon emissions in the eastern region show a high degree of convergence, even in the two most extreme cases. The fit is poorest in the western region, but improves significantly if public's sensitivity to carbon emissions increases. (4)To sum up, some policies that are consistent with the context of the times, such as the East-West regional counterparts and the construction of a new type of power system, are worthy of priority consideration by the Government. [ABSTRACT FROM AUTHOR]

• This paper aimed to investigate the interactions between land use cover (LUC) and climate change (CC). • The results showed that the interactions between LUC and CC are both direct and indirect. • Arable and rangeland use changes affect CO2 emission in both direct and indirect ways. • Rising rangelands and reducing forest areas have indirectly increased surface temperature. • CC mitigation policies should be followed by considering both direct and indirect effects. Humanity deals with several challenges in this century such as climate change, land use, and land use/cover change (LUCC). Determining the patterns, developments, and consequences of LUCC issues for the livelihoods of people, especially poor people, is very important. Therefore, this paper aims to investigate the interactions between LUCC and climate change over the period of 1966–2015 (50 years) as a complex system at the global level. CO 2 emissions and surface temperature are considered as the main indicators of climate change (CC). The data were analyzed in time-oriented (time-based) and local or place-oriented (country-based) manners. The results showed that arable and rangeland use changes (LUC) affect CO 2 emissions in both direct and indirect ways. However, the direct effect of rangeland use change is positive, and its indirect effect is negative. In addition, deforestation has increased CO 2 emissions indirectly. LUCC can also change the ability of the ecosystem to deliver services to people, including biodiversity and other resources such as food, fiber, water, etc. Therefore, it is critical to determine the patterns, trends, and impacts of LUCC on CC. Thus, CC mitigation policies should be followed by considering both direct and indirect effects. Without a doubt, this will be realized when the decision and policymakers have a better understanding of the structure and interaction between CC, LUCC, and their components as a whole system. [ABSTRACT FROM AUTHOR]

[Display omitted] • Exploring the industrial water-land-energy-carbon interaction help promote resource conservation and low-carbon development. • Constructing evaluation indicators and decomposition model to assess the carbon emission effect of industrial water and land use. • Industrial carbon emission intensity of water and land use in China increases, and inter-provincial difference expands. • Industrial matching index of water and land resources in China presenting great spatial agglomeration. • Matching index of water and land resources is the main inhibitory factor for all provinces in China. The industrial production requires the input of water, land and energy, and the water-land-energy interaction generates carbon emissions. Investigating the industrial water-land-energy-carbon nexus and exploring the influencing factors of the carbon emissions help promote the intensive use of water resources, land resources, and energy, as well as the low-carbon development of industry. This paper applied kernel density estimation and spatial auto-correlation analysis to explore the spatio-temporal variation of industrial carbon emission effect of water and land use and the industrial matching status of water and land resources in China, and then analyzes the influencing factors to industrial carbon emissions by introducing water and land resource factor into Kaya identity and Logarithmic Mean Divisia Index (LMDI) model. The main conclusions are: (1) From 2006 to 2020, China's industrial carbon emissions increased by 82.02%. The industrial carbon emission intensity of water use (CEIWU) and the industrial carbon emission intensity of land use (CEILU) increased by 158.02% and 55.53% respectively. The inter-provincial difference of CEIWU and CEILU expanded considerably, and the number of provinces with high CEILU had a large increase. (2) The industrial matching index of water and land resources (MIWL) in China showed a fluctuating downward trend, presenting great spatial clustering, and the provincial MIWL were mainly concentrated in the H-H and L-L regions. (3) The contributing and inhibitory effect of each influencing factor varied on regional and provincial level. The economic output of water use is a contributing factor to all regions and provinces, while the MIWL is an inhibitory factor to all regions and provinces. [ABSTRACT FROM AUTHOR]

• Revisit the relationship between population and energy in the late stage of demographic dividend. • Consider the relationship between population and energy from the perspective of metabolism. • Measure metabolic efficiency by combining emergy analysis and the Index-DEA. • Explore the multi-scale response of metabolism to population quality. • Provide a reference for other developing countries with population and energy issues. China's population and carbon emissions will reach two peaks in 2030. Especially in the late stage of demographic dividend, it is necessary to revisit the relationship between population and energy. Metabolism is a critical perspective to help understand the population-energy relationship. This paper establishes a new paradigm for assessing the impact of population quality change on regional metabolism based on panel data from 2003 to 2019 in China. We measure the population quality index (PQI) using the Projection Pursuit method from four aspects and regional metabolic efficiency (RME) using emergy analysis and Index-DEA. Then, the Tobit model is used to explore the impact of population quality change on regional metabolism from a multi-scale perspective. The results show that the influence has some variability at different scales. From the total sample, the response of regional metabolism to population quality is significantly positive. For different PQI regions, the RME has a positive response to PQI in both, but this response is more obvious in advanced PQI regions. For regions in different locations, RME has positive response to PQI in both East and West, but there is no obvious response in the Central. These findings are closely related to global sustainable development. [ABSTRACT FROM AUTHOR]

• Collections of basic research for urban spatial planning. • Literatures of urban agglomerations, urban size, urban compactness, and urban form on carbon emission. • Urban spatial structure and carbon emissions research from a multi-ecosystem interaction framework. Carbon emission reduction is one of the key factors in global climate change and should be an important component in urban planning and management. A full understanding of the relationship between carbon emissions and urban spatial structure is necessary. This paper presents the research on this relationship from the perspectives of urban agglomeration, urban size, urban compactness, and urban form. The results revealed that: (1) there is still a lack of research on the spatial structures of urban agglomerations and their relationship with carbon emissions, so it is urgent to conduct indicators system design and empirical research; (2) two-dimensional indicators have frequently been used to describe urban form and urban compactness, but three-dimensional indicators, such as volume ratio and floor height, are also important indicators for policymaking; (3) higher urban complexity, irregularity, and fragmentation were believed to contribute to urban carbon emissions because of the lack of continuity and connectivity. This paper also proposes a comprehensive analytical framework for examining urban spatial structures and carbon emissions. We position the research on the relationship between urban spatial structure and carbon emissions as a bridge between human ecosystems and natural ecosystems. [ABSTRACT FROM AUTHOR]

• Analyze the synergy of CO 2 emission factors of energy-intensive industries via EMD. • Synergy of CO 2 emission factors: long-term trend, short-term volatility. • Elastic coefficient model is used to reveal the heterogeneity of the 10 factors. • Six industries are divided into "stable and balanced" and "sensitive and uneven". Carbon emission is a global problem that countries around the world are paying attention to and urgently need to be resolved. Under the requirements of China's high-quality economic development and the carbon peaking and carbon neutrality goals, energy-intensive industries, as a key link in the industrial chain and the main source of China's CO 2 emissions, have attracted much attention. This paper made a statistical analysis of the scale development of six energy-intensive industries, and explored the fluctuation characteristics of their carbon emissions. The empirical mode decomposition (EMD) model was further introduced to analyze the and synergistic effect among the driving factors of carbon emission, revealing the synergistic mechanism of long-term trend and short-term volatility of factors. Then, the elastic sensitivity analysis model of carbon emissions in energy-intensive industries was constructed to study the heterogeneity of driving effects of factor indicators. From the perspective of factors driving, this article reveals the heterogeneity of the driving effects of ten key factor indicators including economic level (GDP), urbanization level (UP), industrial structure (IS), technological innovation (TI), and environmental regulation (ER); from the perspective of industry development, this paper summarized the "stable and balanced" and "sensitive and uneven" trends presented by the heterogeneity of the six energy-intensive industries. Combining the characteristics of industry emission fluctuations, driving factors' synergies and heterogeneity, from the perspective of short-term and long-term development, four targeted emission reduction control strategies were proposed to provide reference for the low-carbon development of energy-intensive industries. [ABSTRACT FROM AUTHOR]

China is in a stage of rapid new urbanization, carbon emissions in regions the urbanization stages of which are different exhibit different characteristics. Based on the environmental Kuznets Curve, this paper calculates the carbon emissions of 32 provinces in China over 1978–2014 by IPCC. Using the Auto-regression Distributed Lag Model, we analyze the three-stage dynamic relationship between carbon emissions and urbanization rate in different city groups. And the influence of energy intensity and energy structure on this relationship is analyzed. Our finding shows that: The relationship between urbanization rate and carbon emissions is a three-stage dynamic relationship. In the provinces of Cluster 1 where the proportion of the service industry is the most and the urbanization rate is high, the shape of the three-stage curve is positive increase, positive decrease and negative increase; In the provinces of Cluster 2 which mainly depend on the manufacturing industry and have medium urbanization rate, the shape of the three-stage curve is positive decrease, positive increase and negative increase; In the provinces of Cluster 3 which mainly rely on the tourism industry and the husbandry industry and have lower urbanization rate, the shape of the three-stage curve is positive increase, positive increase and negative decrease. The decreasing effect of energy intensity on the carbon emissions is different significantly under different urbanization stages, and the decreasing effect is significant in the provinces of Cluster 1. The decreasing effect that energy structure has on carbon emissions is not significant, because the fossil fuels-dominated energy structure remains unchanged in China. Above all, this study can provide theoretical guidance for local governments to formulate carbon emission reduction plan in future. [ABSTRACT FROM AUTHOR]

• The PB-HDI is based on Strong Sustainability and Planetary Boundaries. • The PB-HDI incorporates "relative" ecological impacts into human development. • The PB-HDI has made references to Planetary pressures–adjusted HDI. • In terms of the PB-HDI, Costa Rica is ranked as the top country. During the past 30 years, Human Development Index (HDI) has evolved into a widely accepted proxy of human development. Based on Strong Sustainability and Planetary Boundaries, this paper puts forward Planetary Boundaries–adjusted HDI (PB-HDI). The PB-HDI has made references to Planetary pressures–adjusted HDI proposed by the UNDP. Being simple, transparent and theoretically valid, the PB-HDI incorporates "relative" ecological impacts, i.e. ecological impacts beyond planetary boundaries, into human development evaluation. The PB-HDI is the product of adjusted HDI and Ecological Overshoot Index. The adjusted HDI employs Healthy Life Expectancy at Birth to measure medical and health levels and sets a threshold value of Gross National Income per capita; the Ecological Overshoot Index measures "relative" ecological impacts and is constructed by employing consumption-based CO 2 emissions and Material Footprint. Being compatible with intragenerational and intergenerational equity, the PB-HDI advocates achieving higher levels of economic-social development within planetary boundaries. PB-HDI values and ranks of 142 countries in 2015 are different with those of the HDI by a large extent. For the PB-HDI, Costa Rica, Panama, Hungary, Sri Lanka and Argentina perform best and Singapore, Australia, the United States, India and China do not perform well. The paper provides a more scientific and reasonable perspective and index for human development evaluation in the full world and Anthropocene. [ABSTRACT FROM AUTHOR]

• The carbon emission coefficients method and net ecosystem productivity (NEP) were used to evaluate urban carbon emissions and green space carbon sinks, respectively. • A multi-objective programming model (MOP) was constructed to derive the optimal solution of the amount of urban green space based on the carbon balance theory. • Combined with ventilation resistance and species migration resistance assessment, priority distribution areas of green space in 2035 were identified. • The PLUS model was introduced to optimize the spatial pattern of urban green space. • Optimized green space network stability and carbon sinks will be enhanced in 2035. Cities experience the most intensive level of human activity. As a result, more than 60 % of global CO 2 emissions come from urban areas. Urban green space has the dual ecological benefits of increasing carbon sinks and reducing carbon emissions. Creating green space is essential to promoting the development of a low-carbon cycle in a city. Therefore, exploring the quantitative structure and spatial pattern optimization of urban green space from the perspective of carbon balance can effectively improve the total carbon sink of a city. Based on the carbon balance theory, this paper first evaluates the carbon offsetting capability (COC) of urban green space in Beijing in 2020. Then, CO 2 emissions is predicted, COC improvement targets are established, and the quantity of standard green space is calculated under these targets in 2035. A multi-objective programming model (MOP) is constructed to derive the optimal solution to determine the amount of standard green space needed to meet the constraints of urban development planning and maximize the carbon sink. A circuit model is used to identify the priority distribution area of green space in 2035, and the Patch-generating Land Use Simulation (PLUS) model is used to simulate the spatial pattern optimization results. The results show that: (1) CO 2 emissions in Beijing caused by human activities in 2020 totaled about 240.12 million tons, the net absorption of CO 2 of green space was about 8.99 million tons, and the COC was about 3.74 %; (2) in 2035, Beijing's CO 2 emissions will be about 265.40 million tons. Under 5 %, 10 %, 15 %, 20 %, and 25 % COC improvement gradients, the demand for standard green space will be 12,204.80 km2, 12,763.80 km2, 13,353.85 km2, 13,943.90 km2, and 14,533.96 km2, respectively; (3) the results of the Multi-Objective Programming (MOP) model show that the optimal COC in 2035 is 4.19 %, and the standard green space quantity is 13,012.24 km2; (4) In 2035, Beijing's urban green space will be characterized by a network structure of circular radiation. The optimized COC is 4.37 %, and the standard green space quantity is 13,577.86 km2, which is largely consistent with the MOP model's predicted result. This study can provide theoretical and methodological support for urban green space planning, and expand nature-based solutions for the low-carbon development in cities. [ABSTRACT FROM AUTHOR]

• A new Two-dimensional decoupling model was constructed. • The static and dynamic two-dimensional decoupling state of China's 30 provinces from 2000 to 2019 was analyzed. • The decoupling state of economic growth and CO 2 emissions of 30 provinces was discussed in two decoupling models. The traditional Tapio decoupling model can not discriminate the decoupling state of a country or region with multiple levels of economic development. Based on this, this paper introduces the economic growth index into the Tapio decoupling model for the first time, and constructs a new two-dimensional decoupling (TDD) model. Through the TDD analysis of economic growth and CO 2 emissions of 30 provinces in China from 2000 to 2019, the three main conclusions can be drawn: (1) the economic development level of the developed eastern provinces is at the medium or high state and the economic growth does not highly depend on energy consumption; (2) The economy of the central and western provinces is at the low or middle level, their economic growth relies too much on energy consumption; (3) The dynamic path of TDD state of 23 provinces is ascendant and the remaining 7 provinces are fluctuant over the 4 sub-periods. Provincial governments should adjust industrial and energy structures based on their local conditions to reduce carbon emissions, as well achieve the ideal TDD state of economic growth and CO 2 emissions, that is, Strong Decoupling-High economic state (SD-HS). [ABSTRACT FROM AUTHOR]

• Theoretical framework of water-energy-carbon nexus on land use was established. • The carbon emission/absorption and its intensity vary among different land use types. • The carbon emissions from different land use types will reach peak at different time. • It has great potential of carbon emission reduction under the scenario of water-energy-land saving. The rapid urban expansion of China has led to a large amount of water and energy consumption, and caused drastic growth of carbon emissions. Discovering the water–energy–carbon nexus of different land use types helps explain the interactions between resources capacity and environmental effects of land use activities, as well as provides reasonable options for land use management based on water–energy conservation and carbon emission reduction. In this paper, carbon emission/absorption of different land use types was estimated and the water–energy–carbon nexus of different land use types in Zhengzhou was analyzed. Through the SD model, the future carbon emissions of different scenarios were predicted under water, energy, and land constraints. It showed that total carbon emission increased and carbon absorption reduced along with the land use change during the rapid urbanization process of Zhengzhou city. The water and energy consumption and carbon emissions of different land use types were determined by the different intensities and their spatial patterns of human activities. Therefore, carbon emission and its intensity varied with land use types and water–energy–carbon nexus was closely related to land use types. The carbon emission prediction showed that the carbon emission reduction potential was the highest and the peak carbon emission (40.18 × 106 t) occurred in 2025 under a comprehensive scenario, which was designed with water–energy saving and optimization of land use structure. This study could provide references for policy–makers to formulate differentiated strategies for different land use activities, and contribute to realize water–energy saving and efficient utilization of land and the emission reduction of Zhengzhou city. [ABSTRACT FROM AUTHOR]

• Scandinavian countries are fostering a sustainable economy based on renewable energy. • The investments on renewable sources has positively affected the investments in this energy and GHG emissions. • A bidirectional causal relationship exists between renewable energy consumption and CO 2 emissions. • A causal relationship exists between renewable energy consumption and economic growth. Climate change presents the greatest challenge facing all countries of the world in the new millennium. Among others, objective 13 of the Sustainable Development Goals (SDGs) aims at adopting urgent measures to contrast climate change and its consequences. Part of the decline in the global growth of emissions has been the increase in using renewable energies. In this context, the relationship among GDP, CO 2 emissions, and renewable energy use has been investigated in this study, starting from a systematic review that has noticed the presence of three clusters focused on: CO2 emissions, GDP, and energy consumption. Despite the current level of interest in examining the relationship among these variables, there have been few empirical studies. To fill this knowledge gap, this paper has been focused on the Scandinavian countries, where the use of renewable energies has steadily increased, developing novel panel analysis estimates. Using a dataset of these five economies over a 1990–2018 time period, several panel data tests have been carried out, in order to robustly assess the causality issue among renewable energies, CO 2 emissions, and GDP. The results of the empirical analysis imply that renewable energy consumption is a useful policy instrument to reduce CO 2 emissions without adversely affecting GDP growth. The main implications have been that the decrease of CO 2 emissions, by increasing renewable energy use, can guarantee high levels of energy efficiency and economic growth. These empirical findings help design innovative energy policy roadmaps and accelerate the ecological transition through the promotion of renewable energy and the reduction of GHG emissions. [ABSTRACT FROM AUTHOR]

Based on the two-level Logarithmic Mean Divisia Index (LMDI) method and Tapio index, this paper constructed an expanded decomposition model for decoupling elasticity and effort index of industrial carbon emissions, and further quantitatively analyzed the delinking indicators on industry growth and environmental pressures in Beijing–Tianjin–Hebei (BTH) economic band from 1996 to 2010. The results indicate that: (1) together with the rapid economic growth, industrial energy-related carbon emissions in BTH region rose from 44477.43 × 10 4 t in 1996 to 105602.33 × 10 4 t in 2006 and then declined to 64426.71 × 10 4 t in 2010, with an average annual growth rate of 2.94%. Our results also show that carbon emissions in BTH region were dominated by the secondary industry, which accounted for about 80% of total carbon emissions. (2) “Weak decoupling” was the main characteristic during the 9th Five-Year Plan period (1996–2000) and 11th Five-Year Plan period (2006–2010), particularly “strong decoupling” in 2007; while “weak coupling” was the main characteristic during the 10th Five-Year Plan period (2001–2005). (3) The rapid economic growth was the main factor responsible for industrial decoupling blocking. The energy structure and energy intensity made significant contributions to the industrial decoupling progress. The industrial structure effect had varied considerably over the years without showing a clear trend. [ABSTRACT FROM AUTHOR]

• China is a net exporter of carbon in Harvested Wood Product sectors. • Carbon emissions in China's Harvested Wood Product sectors are caused mainly by exports. • The US is China's largest importer of CO 2. • The furniture sector exports the most CO 2 among all Harvested Wood Product sectors. International trade of goods is also flows of CO 2. As a key exporter of harvested wood products (HWPs), China emits a large amount of CO 2 to meet the consumption of other countries. This article uses an MRIO model to quantify the embodied CO 2 emissions of China's HWP sectors in international trade based on the WIOD database. China's CO 2 emissions from a production perspective were 121.25 Mt, more than four times those from a consumption perspective. China was a net exporter for embodied CO 2 in HWP sectors, while the US was China's largest importer. In addition, the furniture sector exported more embodied CO 2 by international trade than those from the wood and paper sectors. Based on the unbalances of exports and imports, establishing a common international standard to share the responsibility for CO 2 emissions is of great urgency in HWP sectors. This article facilitates promoting CO 2 emission reduction in HWP sectors and references China's future participation in international trade and environmental negotiations. [ABSTRACT FROM AUTHOR]

• Impact-based water footprint of rolled steel production is first performed. • Fossil energy footprint and carbon footprint are evaluated by using LCA. • Maritime transport, iron power and direct emissions are major contributors. • Adjustment of industrial structure is essential for carbon mitigation of steel industry. The Iron and Steel Industry (ISI) is a difficult-to-mitigate sector that has considerable contributions to energy consumption, water pollution, and CO 2 emissions. Despite broad interest in energy consumption and CO 2 emissions generated by steel production, there are few impact-oriented Water Footprint (WF) assessments of steel production. This study evaluates the water, carbon and fossil energy footprints generated from rolled steel production on the basis of life cycle assessment. Key contributors attributing to those environmental impacts are also identified so as to explore their potential mitigation. Using the Monte Carlo model, an uncertainty analysis is conducted with the aim of providing reliable results for decision-making. Results show that the impact per ton China's rolled steel production has on global warming, human health, ecosystem quality, and resources is 4.40 × 103 kg CO 2 eq (GSD2 = 1.24), 4.15 × 10-3 Disability Adjusted of Life Years (DALY) (GSD2 = 1.31), 7.08 × 10-5 Species·yr (GSD2 = 2.04), and 4.39 × 104 MJ (GSD2 = 1.28), respectively. Global warming and freshwater ecotoxicity is the dominant contributor to the damage impact on human health and ecosystem quality, respectively. Results also show that the transport (freight ship) of raw materials and the production of iron powder are the two main contributors to the overall impact on the environment that rolled steel production has. Several policy recommendations are proposed in this paper, which focus on adjusting the steel industry's industrial structure, promoting the national-level carbon emissions trading in the ISI, promoting international cooperation, and improving public awareness of green and low-carbon development. Such policy recommendations provide decision-makers with valuable insights, which thus allow them to prepare the appropriate mitigation policies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Divided the ERP implementation period into two stages based on the characteristics of land use changing. • A Programs Driving Force Model is modified to evaluate the driving effect of ERP on land use. • Prediction model and field investigation were used to estimate the carbon sequestration. • ERP-driven carbon stocks accounted for 65% (691.6Tg) from 2000 to 2030. Ecological Restoration Programs (ERP) is an effective approach to increase carbon sequestration in terrestrial ecosystems to mitigate climate change, but it's still controversial whether they are as important to carbon sequestration in areas with higher rainfall and accumulated temperature as they are in areas with low rainfall and accumulated temperature. In this study, we evaluated the influences of ERP on land use change (LUC) and carbon sequestration of terrestrial ecosystems in the red soil hilly region (RSHR) of China with higher rainfall and accumulated temperature. In view of the differences in LUC caused by ERP, the change point of ERP implementation was quantitatively determined in 2000 according to the characteristics of LUC, and divided the ERP implementation period into two stages: the early stage (1985–2000) and the mature stage (2000–2015). The Program Driving Force Model (PDFM) modified in this paper shows that ERP such as the Natural Forest Protection Program implemented in the mature stage has a greater driving effect on land use (+18%). Based on the three carbon pools (above-ground biomass, underground biomass, and soil organic carbon) of the ERP area in the forest land, cultivated land and grassland ecosystem, it is estimated that the total carbon storage of the terrestrial ecosystems generated in the period from 2000 to 2030 amounts to 1057.2 Tg, 65% of which is driven by ERP (691.6 Tg). Our estimates of future terrestrial ecosystem carbon sinks from ERP, reduces further uncertainty to our capacity to predict the future of terrestrial carbon uptake and losses and to China's projected carbon emissions peak in 2030. The quantitative assessment of the driving force of ERP on LUC provides a methodological reference for related researches. The practice of changing land use and increasing carbon sinks through ERP provides guidance for offsetting carbon dioxide emission when China's carbon dioxide emissions is expected to reach the peak by 2030, and striking sustainable development. [ABSTRACT FROM AUTHOR]

[Display omitted] • China's agricultural carbon emission performance (ACEP) has greatly improved. • Technological progress promotes ACEP, but efficiency changes does little. • China's ACEP has an aggregation effect, provinces with lower ACEP are catching up. • China's ACEP is converging and growing at the same time. • China's ACEP will increase further in terms of spatial scope and temporal trends. The spatial characteristics of carbon emissions form the basis for exploring carbon neutrality measures for different regions. As the second largest carbon emission source globally, agricultural carbon emissions should not be ignored. This paper used panel data from 2009 to 2019 to calculate the agricultural carbon emissions of 30 provinces in China, and used the ML index, spatial Morans'I index and three convergence indices to analyze the dynamic changes and spatial pattern of China's agricultural carbon emission performance (ACEP). The results showed that: (i), China's ACEP has increased by 1.5% from 2009 to 2019, following a spatial pattern of eastern (1.041) > western (1.020) > central (0.974);(ii) the contribution of technological progress to China's ACEP was 1.035, exceeding 1, and thus indicating that technological progress can improve ACEP, while the contribution of efficiency changes was only 0.991; (iii) China's ACEP has exhibited a spatial aggregation effect over the last 10 years (P < 0.05), but the value of Moran's I index dropped from 0.265 in 2009 to 0.202 in 2019, which indicates that the spatial aggregation of ACEP is weakening; and (iv) the ML index for 30 provinces in China showed σ convergence, which indicates that the ACEP is improving, while there is absolute β convergence and conditional β convergence, which indicates a "catch-up effect" among regions. The results indicate that China's ACEP is likely to increase further in terms of spatial scope and temporal trends. [ABSTRACT FROM AUTHOR]

• A LEN CO 2 emission estimation model was constructed based on land use data, enhanced vegetation index and night light data. • The distributions of urban, industrial and rural CO 2 emissions in China were estimated respectively. • The spatial and temporal variations were analyzed. • The CO 2 emissions estimated by using three different models were compared. It is difficult to accurately estimate the spatial distribution of carbon dioxide (CO 2) at the grid scale because of the lack of city-level statistical data. In this paper, CO 2 emission regions were devided into urban area, industrial area and rural area in order to accurately calculate CO 2 emissions. In addition, a more accurate calculation model for energy-related CO 2 emissions was proposed through integrating nighttime light datasets and land use data. The maps of estimated CO 2 emissions in different regions were compared and examined through using spatial dependence and grid overlay methods to understand the spatial distribution characteristics and spatiotemporal dynamics of CO 2 emissions in China. The results showed that the model proposed in this study was appropriate and reliable for CO 2 emissions not only in urban and rural areas but also in industrial areas. CO 2 emissions in China were mainly concentrated in the coastal areas and the northern area, and the amount of carbon emissions increased rapidly from 2000 to 2018 in the Middle Yellow River. These results could improve the understanding of regional discrepancies of spatiotemporal CO 2 emission dynamics at gride scale, and provide a scientific reference for the formulation of energy conservation and emission reduction policies by the local government. [ABSTRACT FROM AUTHOR]

• We built a carbon-flow-based production chain network. • We proposed five new indicators to measure the importance of sectors. • We identified the key sectors in the production chains in China. Effectively reducing carbon emissions is a serious issue faced by many countries. Identifying key sectors in terms of carbon emissions in production activities is vital for policy-making regarding energy related carbon emission reductions. This paper built a carbon-flow-based production chain network and proposed five indicators, including direct outflows, direct inflows, self-flows, indirect outflows and indirect inflows of carbon emissions, to quantify the importance of sectors to carbon emissions. The results showed that there were no strong correlations among the different roles of sectors. This independence of sectoral roles indicated that it was necessary to measure the importance of sectors from different perspectives. The results also showed that a few sectors played important roles in carbon emission flows. It indicated that the growth of carbon emissions in China was direct and indirectly driven by a few key sectors. Finally, the results of clustering analysis showed the clusters exhibited significantly different features. Commonly, industries processing primary products played core roles in production chains. Most industries processing deep-processing products had strong inflows of carbon emissions. Such information provides a valuable reference to make carbon reduction policies according to the roles of sectors in the production chains. [ABSTRACT FROM AUTHOR]

• Life Cycle Assessment quantifies CO 2 embodied in the cross-border waste transfer. • The footprint of CO 2 in waste import follows an EKC. • It provides empirical evidence to the "Waste Haven Hypothesis". • This research correctly measures the environmental pollution of imported waste. • It describes the evolution of the global waste trade network. Many developing countries, such as China, Malaysia, Vietnam, India, etc., choose to prohibit waste import. Does this mean the inflection point of the Environmental Kuznets Curve (EKC) in these countries has arrived? In this paper, we first calculate the carbon dioxide emission and energy consumption in major resource-based wastes of 134 countries using the Life Cycle Assessment (LCA) method. Second, we test whether the relationship between CO 2 (energy consumption) and GDP per capita confirms an EKC's inverted U-shaped curve. Empirical results show that: (1) the relationship between embodied CO 2 emissions (energy consumption) of imported wastes and GDP per capita of a country confirms EKC in various robustness checks, including using an alternative, dependent variable and using sub-sample data. (2) The EKC's inflection point on the average embodied carbon dioxide emissions of imported wastes is 36623.88 USD (37181.3 USD for energy). (3) EKC appears in 68 countries out of 134 countries. Most of these 68 countries have crossed the turning point. This research is novel in describing the EKC of waste trade's carbon footprint using the LCA model. This is the first research that provides empirical evidence to the "Waste Haven Hypothesis". This result empirically explains the evolution of the global waste trade network. That is, waste first transfers from the "North" to the "South" (from US to Europe, and from Europe to Japan and Korea), and then it moves again from the "South" to the "South" (from China to other Asian countries). It reflects the evolution of global pollution haven transfer in the pollution-intensitive sector. Previous research failed to do it because the indicators used for waste transfer limits to trade value (weight) that can not correctly measure waste transfer's environmental pollution. An advantage of using LCA is that it better estimates the pollution generated by the recycling sectors. [ABSTRACT FROM AUTHOR]

• Energy sustainability includes security, efficiency, cleanness, low carbon emissions. • Chinese energy sustainability has obvious spatial heterogeneity. • Cleanness and efficiency perform better than security, low carbon emissions in China. • Energy supply, consumption structure and pollution emissions are key impact factors. • Targeted measures are proposed to promote Chinese energy sustainability. Energy sustainability is one of the largest challenges for sustainable development. Energy sustainability in China varies by regions because of the differences in energy resources, technology and economic levels. Previous studies have assessed national energy sustainability, few to none have looked internally on a regional or provincial scale. This paper defined energy sustainability from the four dimensions of security, efficiency, cleanness and low carbon emissions, established an index system and evaluated the energy sustainability levels of 30 provinces in China by the Improved Entropy Weight-Cloud Model (IEW-CM). The results show that: (1) General energy sustainability in Chinese provinces is deficient, and energy cleanness and efficiency perform better than security and low carbon emissions. (2) Chinese energy sustainability has obvious spatial heterogeneity and within the same province, the level is unbalanced in four dimensions. The characteristics of energy sustainability exhibit significant differences in different regions. (3) Energy security and low carbon emissions play a crucial role in achieving energy sustainability. Natural gas deliverability, energy consumption elasticity coefficient, CO 2 emission per unit of energy and share of fossil energy consumption are the primary factors affecting energy sustainability differences among provinces. [ABSTRACT FROM AUTHOR]

[Display omitted] • Random forest and regression tree show high reliability on land use classification. • CO 2 emission is highly influenced by spatial attributes in land use patterns. • NSGA-II is effective on achieving low carbon emission in spatial optimization. • Optimum solutions provide valuable references for land use zoning plan. As the spatial carrier of carbon emission from land ecosystems and human activities, land use plays an important role in representing the spatial distribution of carbon emissions and carbon sinks. The intension of CO 2 emission is closely attached to the fundamental unit of urban form—land use patterns. The exploration of the low-carbon oriented spatial optimization provides new scope for solving the high carbon emission in the urban area through spatial planning. In this paper, Eindhoven in the Netherlands was selected as the case study to implement spatial optimization. A set of parameters containing the spatial attributes of buildings and vegetation were introduced to classify the land use patterns into six categories through a combined random forest algorithm and regression tree approach. The results show the geographic features of vegetation are the crucial factors for the carbon emission. The multi-objective spatial optimization model integrated carbon emission, population, and constraint conditions. It is solved with non-dominated sorting genetic algorithm-II (NSGA-II), in which each gene represents a specific type of land use category. The optimal solutions were incorporated with a regression model to analyze the impact of the variation in each land use category. Three categories were proved to be more influential on carbon emission performance. The optimized land use structure shows the potential to reduce the carbon emission and offer valuable consults to low carbon land use plan. [ABSTRACT FROM AUTHOR]

• Energy rebound effect leads to "green paradox". • The effect of short-term energy rebound effect is greater than the long-term. • Investment bias and factor market distortion important for policy development. • Government decentralization can suppress the energy rebound effect. Analyzing the influence of energy rebound effect on carbon emissions at different urbanization stages is of great significance to improve the effectiveness of emission reduction polices. In this paper, the energy rebound effect is classified into the short-term energy rebound effect and the long-term energy rebound effect. Based on the environmental Kuznets curve, the nonlinear programming model of urbanization, energy rebound effect and carbon emissions is established to analyzed the influence mechanism of energy rebound effect on carbon emissions at different stages of urbanization. We further discuss how to avoid the impact of energy rebound effect on carbon emission reduction. The results show that: (1) The influence of short-term energy rebound effect on carbon emissions is greater than long-term energy rebound effect, and both of them decrease with the increase of urbanization rate. (2) The "discretionary" and "hitchhiking" behavior of local governments in formulating environmental policies will lead to the "Matthew Effect" of energy rebound effect. (3) Investment bias and factor market distortion could increase energy rebound effect, and they have obvious substitution effect. (4) The decentralization of power by local governments is an effective measure to suppress energy rebound effect. The analysis above could help local governments to formulate policies, suppressing the influence of energy rebound effect and promoting carbon emission reduction. [ABSTRACT FROM AUTHOR]