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• Introduce the diphenyl ether fragments into the structure of boscalid, design and synthesize 24 nicotinamide derivatives. • Several highly promising antifungal compounds have been discovered in the synthesized title compounds. • Theoretical calculation reveals the structural characteristics of biphenyl, diphenylamine and diphenyl ether fragments, and offers guidance for further optimization and design of compounds with higher antifungal activity. • Biological evaluation indicates that the title compound 7g is expected to become a candidate for SDHIs with high efficiency and low toxicity. To obtain nicotinamide fungicides with higher inhibitory activity against Rhizoctonia solani , 24 nicotinamide derivatives were designed and synthesized by introducing the diphenyl ether fragments into the structure of boscalid. Biological assays revealed that the obtained compounds exhibited varying degrees of antifungal activity. Among them, compound 2-chloro- N -(2-(2,4-dichlorophenoxy)phenyl)nicotinamide (7g) exhibited prominent biological activity against R. solani (EC 50 = 0.034 mg/L), surpassing the inhibitory effect of boscalid. Experimentally, compound 7g was a promising fungicide with low toxicity that targeted succinate dehydrogenase, as confirmed by the tests of cytotoxicity, inhibition of enzyme activity, mitochondrial membrane potential, and mycelial respiration. This study highlighted the potential of nicotinamide derivatives containing diphenyl ether fragments designed based on theoretical calculations as succinate dehydrogenase inhibitor (SDHI) fungicides. [Display omitted] [ABSTRACT FROM AUTHOR]

[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 challenge of global warming requires deep decarbonization of the electricity system, which can be achieved with a higher share of renewables in the electricity mix. However, the driving mechanism for renewable penetration in electricity production has not been clearly revealed so far. Using the data of the European Union (EU) during 2001–2017, this paper aims to investigate the spatial-temporal heterogeneity and dynamic evolution mechanism of renewable electricity penetration. To this end, from the investment-production-consumption perspective, renewable electricity generation is decomposed into seven factors through temporal and spatial factor decomposition methods. Then, this paper integrates the factor decomposition results into a coupling elasticity analysis to study the dynamic evolution mechanism of renewable penetration in electricity production. The results are as follows. (1) Investment structure contributes the most to increasing renewable electricity generation, while the energy intensity effect and renewable generation-to-capacity ratio effect are the main reasons for the reduction in renewable electricity generation. The effect of technological structure is minor. (2) The economic scale effect and the investment structure effect are dominating factors accounting for the regional difference of renewable power generation between the EU countries and the benchmark region. (3) The progress of renewable electricity penetration accelerates over time for the EU, western EU, and eastern EU. The Western EU has realized remarkable progress in facilitating renewable electricity penetration during 2009–2017. (4) On the whole, the investment effect makes the largest contribution to promoting renewable electricity penetration, while the production effect is the major inhibiting factor, showing that there is considerable potential in renewable generation efficiency improvement in the EU. This paper provides insightful implications for energy policies aimed at the shift towards a low-carbon electricity system in the EU, which also gives a reference for other economies. [ABSTRACT FROM AUTHOR]

Fatty acids (FAs) have attracted many interests for their pivotal roles in many biological processes. Imbalance of FAs is related to a variety of diseases, which makes the measurement of them important in biological samples. Over the past two decades, mass spectrometry (MS) has become an indispensable technique for the analysis of FAs owing to its high sensitivity and precision. Due to complex matrix effect of biological samples and inherent poor ionization efficiency of FAs in MS, sample preparation including extraction and chemical derivatization prior to analysis are often employed. Here, we describe an updated overview of FA extraction techniques, as well as representative derivatization methods utilized in different MS platforms including gas chromatography-MS, liquid chromatography-MS, and mass spectrometry imaging based on different chain lengths of FAs. Derivatization strategies for the identification of double bond location in unsaturated FAs are also summarized and highlighted. The advantages, disadvantages, and prospects of these methods are compared and discussed. This review provides the development and valuable information for sample pretreatment approaches and qualitative and quantitative analysis of interested FAs using different MS-based platforms in complex biological matrices. Finally, the challenges of FA analysis are summarized and the future perspectives are prospected. [ABSTRACT FROM AUTHOR]

Energy security, energy equity, and energy greening are three crucial challenges (i.e., energy trilemma, ET) of a nation's energy system while achieving economic growth with the reduction of carbon emissions. Investigating the impact of ET on green total-factor productivity (GTFP) helps provide essential implications for facilitating collaborative low-carbon economy and sustainable energy development. Using the data of 142 economies spanning 2008–2018, the country-level GTFP is evaluated based on a super-efficiency Epsilon-Based Measure (EBM) model with window analysis. Then, this study analyzes how ET influences GTFP, and further examines the heterogeneous effect of ET through the dynamic panel threshold model. The results specify that ET has a positive effect on GTFP, which is robust to various econometric techniques, model specifications, alternative measurement of GTFP, carbon emission intensity as a surrogate indicator for GTFP, and covariates choices. Notably, each branch of ET is also positively related to GTFP. Further threshold effects analysis discloses the differentiated impact of ET on GTFP. When the ET exceeds its threshold, the positive impact of ET on GTFP becomes more effective. Only when GDP per capita and industrial structure are within their higher intervals will ET has a significant and larger positive effect on GTFP. • The impact of energy trilemma on green total-factor productivity is investigated. • Spatial-temporal dynamics of green total-factor productivity are studied globally. • Energy trilemma has a positive impact on green total-factor productivity. • Separate energy trilemma sub-indicators all positively influence GTFP. • The ET-GTFP nexus is contingent on ET, industrial structure, and GDP per capita. [ABSTRACT FROM AUTHOR]

Under the background of global climate change, China has been confronted with the dual pressure of CO 2 emissions reduction and PM 2.5 pollution control. This research aims to explore the mechanism of changes in PM 2.5 emissions, which are the key airborne pollutants causing haze. Furthermore, it quantifies the impacts of CO 2 emissions reduction activities on PM 2.5 emissions reduction. This study takes aggregate PM 2.5 emissions instead of PM 2.5 concentration index as the research object. Based on an extended kaya identity, LMDI approach is first performed to decompose the changes of PM 2.5 emissions during 1998–2014, taking into consideration the synergistic effect of carbon emissions on PM 2.5 emissions. Furthermore, following LMDI decomposition, this study adopts the econometric methods to quantify the synergistic effect of CO 2 emissions reduction on PM 2.5 emissions reduction over the period 1999–2014. The empirical results are as follows: (1) the LMDI decomposition results specify that the synergistic emissions reduction accounts for the most of the reduction in PM 2.5 emissions. In addition, energy intensity changes also contribute to the reduction in PM 2.5 emissions; (2) by contrast, it is found that the economic development effect is the main factor resulting in the increase of PM 2.5 emissions, while the contributions of the energy emission intensity effect and population effect to PM 2.5 emissions changes are relatively little; (3) all the models show CO 2 emissions reduction activities will significantly contribute to PM 2.5 emissions reduction; (4) for every 10,000 t increase in CO 2 emissions reduction, PM 2.5 emissions reduction will increase by 3.3 t, and the potential for synergistic emissions reduction of PM 2.5 differs distinctly among different provinces; (5) technological progress and population density positively influence PM 2.5 emissions reduction, while coal consumption rate has a negative impact on PM 2.5 emissions reduction, in addition, there is an inverted U-shaped curve relationship between per capita GDP and PM 2.5 emissions reduction. • Considering the synergistic effect of CO 2 emissions in an extended Kaya identity. • LMDI decomposition is performed to study PM 2.5 emissions changes in China. • Furthermore, five proxy variables are introduced in econometric analysis. • CO 2 emissions reduction will significantly contribute to PM 2.5 emissions reduction. • EKC hypothesis is confirmed for per capita GDP and PM 2.5 emissions reduction. [ABSTRACT FROM AUTHOR]

The power sector is a linchpin of global carbon mitigation goal, and renewables are increasingly the preferred source of new electricity generation globally. It is necessary to uncover the driving mechanism of renewable energy penetration and its impact on deep decarbonization of the power sector. To this end, this research utilizes the production-theoretical decomposition analysis to attribute the change in renewable electricity generation to nine drivers. Then, a system dynamics model is established to track the trajectory of carbon emissions in the power sector under multiple scenario settings of renewable energy penetration. Additionally, cross-country heterogeneity analysis is conducted between China, the USA, the UK, and Germany. The results indicate that: (1) The electricity consumption scale plays a decisive role in driving China's renewable electricity generation during 1995–2018, while electricity productivity and output productivity are the major negative contributors. (2) In the UK, the USA, and Germany, electricity productivity and output productivity act as the main promoters of renewable electricity generation. (3) The decline in China's two productivity effects is primarily attributed to the drop of technical efficiency. However, technology progress in these developed countries contributes to increasing the two productivity indexes. (4) The IRENA roadmap scenario (40%) is not enough to peak carbon emissions in China's power sector before 2030, while 46.3% of renewables in electricity generation is sufficient to achieve this goal. This research provides an insightful roadmap for facilitating renewable energy transition and decarbonizing the power sector. [Display omitted] • Account for the changes in renewable power generation by PDA-LMDI decomposition. • Reveal the differences in the driving factors between China and top renewable markets. • Simulate the impact of renewable energy penetration on decarbonizing the power sector. • The IRENA renewable energy roadmap is not enough to peak carbon emissions by 2030. • The increasing electrification brings some uncertainties to the carbon peak scheme. [ABSTRACT FROM AUTHOR]

• Reveal the complex mechanism in coupling developing CCUS and renewable energy. • Assess the economic effects of CCUS and renewable energy penetration by a CGE model. • Identify the optimal policy intensity to deploy CCUS and renewable energy in power sector. • Explore the impact of CCUS application on carbon peak in China's new power system. • Building a green energy dominated power system is harder than the carbon peak target. Carbon capture, utilization, and storage (CCUS) is identified as a critical decarbonization technology that is expected to be applied globally on a large scale, especially in a thermal power-dominated power system. To achieve a carbon peak in the power sector, assessing the environmental and economic effects, including the costs of CCUS application and the decarbonization efficiency, is essential for making a reasonable CCUS policy. However, there is still a research gap in identifying the proper CCUS application intensity considering the dynamic changing renewable energy percentage and power quantity. Therefore, this study constructs a "dynamic system- computable general equilibrium" (SD-CGE) model to reveal the industrial and macro impacts of CCUS application policies by comprehensively evaluating the social, economic, and environmental effects of CCUS application scenarios where different policy intensities are set. Using China as a typical case, the results show that, first, the policy of applying CCUS combined with renewable energy penetration will contribute to reaching the power sector's carbon peak, however, building a new power system dominated by green power is a more challenging goal. Second, regarding the CCUS application intensity combined with renewable power penetration rate, it is proper to suggest setting the policy intensity as "medium-medium" for the optimal environmental and economic effects. Consequently, the power sector's carbon peak year will be 2025, while green power will dominate the power supply in 2030. Third, through the model's validation check, the models and results in this study are shown to be accurate and robust. This study provides policy support for constructing China's new power system, is conducive to achieving the national carbon peak goal, and provides other countries with theoretical tools to study similar problems. [ABSTRACT FROM AUTHOR]

The world has witnessed a surge in renewable power installed capacity in recent years, and there is an emerging trend of renewable penetration in electricity production. However, there is a lack of quantitative comparative study on disaggregated renewable power sources concerning their generation efficiency performance, regional heterogeneity, development potential, and influencing mechanism in the existing literature. In the case of China's 30 provinces, this paper evaluates the hydropower, solar power, and wind power generation efficiency by stochastic frontier analysis method, and then reveals the distribution characteristics and deployment potential of different renewable sources. Furthermore, from the perspective of spatial heterogeneity, geographical detector model is utilized to study the influence mechanism of the generation efficiency of different renewable sources. The main results are as follows. Firstly, production inefficiency prevails in hydropower, solar power, and wind power generation industries. The installed capacity, utilized hours, and auxiliary power consumption have positive impacts on the three renewable energy sources. Every 1% increase in auxiliary power consumption leads to 0.16% increase in solar power generation, which is quite larger than the increase in hydropower and wind power. Secondly, on average, hydropower has the highest level of generation efficiency, followed by wind power and solar power. Kernel density curves indicate the generation efficiency of hydropower, solar power, and wind power displays distinct aggregation characteristics. Different energy types show significant regional differences in deployment potential. Thirdly, annual precipitation accounts for 76% of the spatial heterogeneity in hydropower generation efficiency, followed by hydropower technology innovation and power structure. As for solar power generation efficiency, the most important influencing factors are electricity investment and economic development. By contrast, wind power generation efficiency is primarily affected by power structure, electricity investment, and urbanization. Additionally, there exist distinct synergistic effects among different variables. These results provide insightful policy support for the improvement of renewable power generation efficiency. The study can be extended to the global scale using country-level data. • Stochastic frontier analysis is performed to study renewable generation efficiency. • Production inefficiency prevails in three renewable power generation industries. • Hydropower generation efficiency is higher than wind power and solar power in China. • Different energy types show significant regional differences in deployment potential. • The driving factors of generation efficiency are examined by geographical detector. [ABSTRACT FROM AUTHOR]

• GDIM is used to investigate the influencing factors of energy-related PM 2.5 emissions in China. • A novel decoupling indicator is constructed based on GDIM decomposition results. • The increase of PM 2.5 emissions is primarily due to output scale and energy use effects. • Emission intensity and emission coefficients are the main factors decreasing PM 2.5 emissions. • Technical effect plays an important role in promoting the decoupling of PM 2.5 emissions. Haze pollution has become a severe threat to China's sustainable development. It is crucial to provide a complete explanation for the mechanism behind the changes in PM 2.5 pollution and explore how to decouple economic growth from PM 2.5 pollution. Compared with the traditional decomposition analysis methods, the Generalized Divisia Index Method (GDIM) integrates multiple influencing factors and provides a fuller understanding of the influence mechanism of PM 2.5 pollution. In addition, the decomposition of the monitored PM 2.5 concentration in previous studies leads to ambiguous economic meanings of some relative impact indicators. In this paper, GDIM decomposition is applied to study the driving factors of energy-related PM 2.5 emissions changes in China. Then, based on the GDIM decomposition results, this paper constructs a novel decoupling indicator to study the decoupling relationship between PM 2.5 emissions and economic growth, and the contributions of the technical and non-technical factors to the decoupling indicator are quantified. The main results are summarized as follows. (1) The output scale effect and energy use effect are the main reasons for the increase of PM 2.5 emissions, while the emission intensity effect and emission coefficient effect make major contributions to decreasing PM 2.5 emissions. The effects of population, per capita emissions, per capita output, and energy intensity are relatively minor. (2) China and its three economic regions move from strong decoupling in 1998–2000 to weak decoupling during 2000–2010, and then into strong decoupling over the period of 2010–2014. (3) Technical effect plays an important role in promoting the decoupling between PM 2.5 emissions and economic growth, but its contribution shows a downward trend over time. In contrast, non-technical effect hinders the decoupling process, and its contribution is decreasing from 2000 to 2014. This paper is supportive for formulating emissions reduction policies and improving China's air quality. [ABSTRACT FROM AUTHOR]

Under the background of climate change, an important role has been identified for power industry in low-carbon transition, it is essential to uncover the evolution of thermal power generation and its nexus with economic growth. In the case of the European Union (EU) 25 countries spanning 1997–2017, we examine the convergence process of thermal electricity generation and its influencing factors through the combined econometric and decomposition analysis, and then investigate the decoupling relationship between economic growth and thermal electricity generation. The results specify that: (1) Both absolute and conditional β-convergence in the share of thermal electricity generation (STEG) are demonstrated across the EU countries, and there exists an inverted-U shaped relationship between economic growth and STEG. (2) LMDI decomposition shows economic growth and electricity security contribute to increasing thermal electricity generation in most EU countries, while the substitution effect, electricity intensity and technological structure negatively influence thermal electricity generation. (3) The number of EU countries in strong decoupling rises from 3 during 1997–2007 to 18 during 2007–2017. (4) Most EU countries have made efforts to promote the decoupling of thermal electricity generation except France and Portugal. Seeking alternative electricity sources instead of electricity import is the most effective decoupling effort. • The convergence in STEG across EU countries is examined by GMM methods. • Thermal electricity generation is decomposed by index decomposition analysis. • EKC hypothesis is verified for economic growth and STEG. • The decoupling effect of thermal electricity generation is explored. • Alternative electricity development instead of electricity import should be enhanced. [ABSTRACT FROM AUTHOR]

Some thiazolamide derivatives containing diphenyl ether moiety were designed, synthesized, and studied for their antifungal activity and cell cytotoxicity. Of these, several compounds II possessed excellent activity against Rhizoctonia solani, which was better than that of boscalid and comparable to thifluzamide. Besides, the cytotoxicity test showed that the optimal compound IIf had lower cytotoxicity than thifluzamide in general. Further, molecular docking could give a reasonable explanation for the high activity of the target compounds. The above results indicated that thiazolamide derivatives containing diphenyl ether moiety are valuable for in-depth study to obtain high-efficiency and low-toxicity green pesticides. A series of new thiazolamide derivatives containing diphenyl ether moiety were designed and synthesized. The biological evaluation indicated optimal compound IIf displayed not only high fungicidal activity but also lower cytotoxicity. Besides, molecular docking gave a reasonable explanation for the high activity of the target compounds. [ABSTRACT FROM AUTHOR]

The residential sector has become the second largest energy consumer in China. Urban residential energy consumption (URE) in China is growing rapidly in the process of urbanization. This paper aims to reveal the spatiotemporal dynamic evolution and influencing mechanism of URE in China. The spatiotemporal heterogeneity of URE during 2007–2018 is explored through Kernel density estimation and inequality measures (i.e., Gini coefficient, Theil index, and mean logarithmic deviation). Then, with several advantages over traditional index decomposition analysis approaches, the Generalized Divisia Index Method (GDIM) decomposition is employed to investigate the impacts of eight driving factors on URE. Furthermore, the national and provincial decoupling relationships between URE and residential income increase are studied. It is found that different provinces' URE present a significant agglomeration effect; the interprovincial inequality in URE increases and then decreases during the study period. The GDIM decomposition results indicate the income effect is the main positive factor driving URE. Besides, urban population, residential area, per capita energy use, and per unit area energy consumption positively influence URE. By contrast, per capita income, energy intensity, and residential density have negative effects on URE. There is evidence that only three decoupling states, i.e., weak decoupling, strong decoupling, and expansive negative decoupling, appear in China during 2007–2018. Specifically, weak decoupling is the dominant state among different regions. Finally, some suggestions are given to speed up the construction of energy-saving cities and promote the decoupling process of residential energy consumption in China. This paper fills some research gaps in urban residential energy research and is important for China's policymakers. [ABSTRACT FROM AUTHOR]

Green technology improvement is critical in promoting green development and mitigating negative externalities. Exploring the effect of economic growth pressure (EGP) on green technology innovation (GTI) is important for coordinated economic growth and green transformation. Using the data from 285 cities in China during 2006–2018, this study investigates the influence of EGP on GTI by taking the difference between economic growth target and previous year's actual growth rate to represent the EGP. The results indicate that EGP negatively affects GTI. When there is a 1% increase in EGP, green patent applications will fall by 3.2%. Furthermore, the heterogeneity analysis indicates that the negative effect of EGP is especially significant in western China compared with eastern and central regions. In addition, we find various nonlinear moderating effects between EGP and GTI by using panel threshold model. Specifically, EGP and GTI show an inverted U-shaped relationship with EGP increasing. Meanwhile, only when environmental regulation, government support, and financial development cross the thresholds will EGP have a significant role in promoting GTI. This study provides helpful implications for decision-makers to adopt a more reasonable combination of policy tools to achieve economic growth targets and low-carbon transformation. • Investigate the impact of economic growth pressure on green technology innovation. • 1% increase in EGP leads to a 3.2% drop in GTI. • EGP and GTI show an inverted U-shaped relationship. • EGP promotes GTI only when REG, GS, and FIN are within a high threshold range. [ABSTRACT FROM AUTHOR]

Rapid urbanization has altered landscape patterns and ecological functions, causing a decline in ecosystem service and generating many ecological and environmental issues. Studying the spatiotemporal interaction between urbanization and ecosystem service (ES) can provide effective supports for regional sustainability and policy formulation. This research utilizes the Yangtze River Economic Belt (YREB) as a case to analyze the spatiotemporal interaction between multi-urbanization indicators and multi-ESs over a large-scale region. The results show that the urbanization process in the YREB evolves from a rapidly growing state to a steady state with a slower rise. The urbanization level of the Yangtze River Delta urban agglomeration is relatively higher than the other regions. The distribution pattern of urbanization shows an overall characteristic of lower urbanization in the west and higher in the east. From 2009 to 2016, ecosystem service value (ESV) in the YREB decreased first and then increased, ESV in 2016 showed a reduction of 12.768 billion yuan compared with the 2009 level. ESV increases gradually from highly urbanized areas to those with lower levels of urbanization. Areas with high ESV levels are distributed at the middle reaches of YREB. There is a U-shaped curve relationship between urbanization and ESV, the ESV sharply increased when the urbanization index exceeded 0.6 in 2012. Land urbanization has the greatest impact on ESV among the four subtypes of urbanization indicators. Urbanization and ESV show the synergy relationship mostly in the eastern region, accounting for 18.18% of the total 110 cities. By contrast, they present the trade-off relationship in northern, southern and central regions, occupying 47.27% of the total observations. This study is helpful to provide scientific suggestions regarding the development of new urbanization, the protection of ESV, and the issue of how to achieve synergistic and sustainable development between them. • In 2009–2016, urbanization in YREB transits from rapid growth to a steady increase. • The ESVs of the YREB decreased first and then increased from 2009 to 2016. • Urbanization and ESV in the YREB showed a U-shaped curve relationship in 2009–2016. • Land urbanization has the greatest impact on ESV from 2009 to 2016. [ABSTRACT FROM AUTHOR]

Understanding the spatial distribution characteristics of ecosystem service value (ESV) and their underlying driving factors is critical for ecosystem service management. Using three national-level urban agglomerations in the Yangtze River Economic Belt, Cheng-Yu (CY), Middle-Reach Yangtze River (MRYR), and Yangtze River Delta (YRD), as a case study, this paper quantifies the ESVs and the spatial distribution characteristics, analyzes the driving factors of ESVs using the stochastic impacts by regression on population, affluence, and technology model (STIRPAT). The results specify that: (1) Over the study period 2009–2016, the total ESV of the three urban agglomerations decreases by 5498.70 million USD. The regulating, supporting, and cultural service decrease by 4607.60, 2648.01, and 1182.25 million USD, respectively, while the provisioning service increases by 2795.15 million USD. (2) ESV in MRYR undergoes the largest reduction of 4269.70 million USD, followed by CY and YRD with 1015.66 and 213.35 million USD from 2009 to 2016. (3) In 2016, among the 70 cities at the prefecture level or above, the cities with high total ESV, per unit area ESV, and per capita EVS are mainly distributed to the areas of the south Yangtze River of MRYR and YRD, MRYR, and MRYR and YRD, respectively. (4) In general, land use and cover, population are the main factors affecting ESVs, followed by economic, social and political factors. Among the three urban agglomerations, population, land use and cover basically have the equally important impacts on ESV in CY; land use and cover, especially the proportion of urban construction land has the greatest impact on ESV in MRYR; population, especially the urbanization rate has the greatest impact in YRD. The comparative analysis of the driving factors of ESVs in different regions is helpful to propose differentiated ecological protection policies and promote the increase of ESV accordingly. Unlabelled Image • ESVs in CY, MRYR, and YRD urban agglomerations all decreased from 2009 to 2016. • Land use/cover change and population are key factors to drive ESV change. • Driving factors on the ESVs in CY, MRYR, and YRD have regional differences. [ABSTRACT FROM AUTHOR]

Under the premise of ensuring economic growth, how to achieve the maximum reduction of PM 2.5 has become one of the important tasks in China. In this study, we investigated the influencing factors of PM 2.5 by employing the Logarithmic Mean Divisia Index (LMDI). The sensitivity of PM 2.5 to various emission reduction factors was also explored. Then, by constructing an indicator of PM 2.5 decoupling efforts (PDE), PDE of different regions and the degree of efforts caused by various factors were explored. Finally, the spatiotemporal heterogeneity of internal and external factors influencing PDE was explored using the Geographically and Temporally Weighted Regression Model (GTWR). The main results are as follows. (1) The emission coefficient was the main negative driving factor of changes in PM 2.5 , and per capita GDP was the main positive driving factor. (2) The sensitivity of PM 2.5 to emission reduction factors in most regions followed the ranking: emission coefficient > energy intensity > population. In the analysis of the sensitivity of PM 2.5 to various emission reduction factors throughout the country, the sensitivity of the emission coefficient was highest in Shandong province. (3) From 2000 to 2014, all provinces made decoupling efforts (PDE>0), among which PDE in Guizhou province were the largest. According to various indicators of decoupling efforts, the contribution of decoupling effort of emission coefficient was the largest. (4) All influencing factors exhibited spatiotemporal heterogeneity. In most regions, the interaction between environmental regulation and industrial structure had a positive impact on PDE, which verifies the existence of the "Porter Hypothesis". However, in some central and western regions, this interaction exhibited a dynamic change from negative to positive. • Decomposition, Sensitivity analysis, Decoupling and GTWR are employed. • The sensitivity of the emission coefficient is the strongest. • PDE in Guizhou province are the largest. • "Porter Hypothesis" exists in most regions. [ABSTRACT FROM AUTHOR]