Urban population density and energy conservation: Empirical evidence from 276 cities in China.

Reducing urban energy consumption is a crucial step towards achieving sustainable urban development. Urban energy plays a fundamental role in urban development, and while previous studies have examined the relationship between population size and energy conservation, the impact of increasing population density on per capita energy consumption (PCEC) remains unclear. To achieve urban energy conservation in China, it is vital to comprehend this significant relationship. This study constructs a spatial regression model to examine the relationship between population density and PCEC using 9 years of balanced panel data from 276 cities to fill a gap in the literature. The results of spatial autocorrelation indicate a significant negative relationship and heterogeneity between population density and PCEC. The results of spatial regression show that for every 1% increase in population density, there is a subsequent increase in PCEC of 0.074%. Our findings suggest that lower PCEC correlation is associated with higher urban population density. This study can be a reference for policymakers seeking new energy conservation strategies for urban development.

Agricultural HANPP Embodied in Consumption: Tracing Pressure on Ecosystems Based on an MRIO Analysis.

Agriculture is one of humankind's most significant sources of biomass; it also places tremendous pressure on ecosystems through its increasing demand for agricultural products. However, few studies have assessed human pressures on ecosystems from agricultural production and consumption based on a whole-supply-chain perspective. Based on the concept of human appropriation of net primary productivity (HANPP), we evaluate trends of agricultural HANPP embodied in consumption from a global perspective and trace the pressure from agriculture production that is exerted on the environment using an environmentally extended multiregional input-output (MRIO) model. The results show that agricultural HANPP embodied in consumption accounted for over two-thirds of total HANPP but brought about less than 7% of global value added. India, Brazil, and China were found to have the highest level of agricultural HANPP embodied in consumption. Agricultural net exporters were found to usually be low- and lower-income countries, while net importers are found to be high-income countries. According to the driving factor analysis, high-income and low-income countries should cooperate by adjusting consumption patterns and sharing agricultural technology to alleviate the pressure from agricultural production. Our study highlights the importance of agrarian expertise sharing and the need to develop sustainable and green agricultural production.

Investigation on Thermal Barrier Coating Damages for Ultrafast Laser Drilling of Cooling Holes.

To mitigate the challenges pertaining to coating damage and processing defects arising from the utilization of ultrafast laser drilling for microhole creation in thermal barrier coatings (TBCs), thereby exerting substantial influence on the long-term durability of these microholes, the investigation proposes a comprehensive methodology. It encompasses the design of a two-factor four-level full factorial experiment and the execution of experimental research on picosecond laser drilling of TBC microholes. By meticulously analyzing the morphology of the microholes and the coating interface, the damage mechanisms associated with picosecond laser drilling of TBC microholes, as well as the influence of laser process parameters on coating damage, are revealed. The findings reveal that the optimal microhole entrance quality and the lowest roughness along the hole perimeter are attained at a laser power of 12 W and a scanning speed of 320 mm/s. Moreover, at a laser power of 30 W and a scanning speed of 320 mm/s, the minimal crack length on the blunt angle side of the hole and the highest machining quality are observed.

Impact of International Trade on the Carbon Intensity of Human Well-Being.

There has been a longstanding debate about the impact of international trade on the environment and human well-being, yet there is little known about such environment and human well-being trade-off. Here, we explore the effect of international trade on the carbon intensity of human well-being (CIWB) globally under the current global trade system and a hypothetical no-trade scenario. We found that between 1995 and 2015, CIWB of 41% of countries declined and 59% of countries increased, caused by international trade, and this resulted in a reduction of the global CIWB and a decline in CIWB inequality between countries. International trade decreased CIWB for high- and upper-middle-income countries and increased CIWB for lower- and middle-income countries. In addition, our results also show that decreases in emission intensity are the most important driver of lower CIWB and the percentage contribution of emission intensity to the improvement in CIWB increases with income. The reduction of emission intensity, population growth, and increase in life expectancy all contribute to CIWB reduction, while the consumption level is the primary factor driving CIWB growth. Our results underscore the importance of studying the impact of international trade on the CIWB of countries at different stages of development.

Measurement and Coupling Coordination of High-Quality Development in Guangdong Province of China: A Spatiotemporal Analysis.

Regional high-quality coordination plays a crucial role in promoting high-quality national development. Guangdong province is a trailblazer in China's reform and opening-up policies and high-quality development. This study analyzes the high-quality development of the economic, social, and ecological environments from 2010 to 2019 in Guangdong with the entropy weight TOPSIS model. Meanwhile, the coupling coordination degree model is used to investigate the spatial-temporal pattern of the coupling and coordinated development of the three-dimensional system in 21 prefecture-level cities. The results show that the high-quality development index of Guangdong increased from 0.32 to 0.39, a 21.9% increase during 2010-2019. The Pearl River Delta had the highest value of the high-quality development index, whereas Western Guangdong had the lowest level in 2019. Guangzhou, Shenzhen, Zhuhai, and Dongguan are the core cities in the high-quality development of Guangdong, with the index decreasing from the Pearl River Delta estuary cities to the province's edge. We also found that the coupling degree and coupling coordination of high-quality development of the three-dimensional system grew slowly during the study period. Half of the cities in Guangdong entered the stage of benign coupling. Except for Zhaoqing, all cities in the Pearl River Delta have a high coupling coordination degree of high-quality development of the three-dimensional system. This study provides valuable references for the high-quality, coordinated development of Guangdong province and some policy suggestions for other regions.

Do development zones increase carbon emission performance of China's cities?

As a special industrial cluster policy, "development zones" (DZs) represent the growth poles of the regions in which they are located; in China, they fulfill the dual tasks of industrial upgrading and green transformation. While the existing literature has paid close attention to the economic effects of development zones, few efforts have been made to examine their climate effects. Therefore, in the context of China's efforts to achieve "carbon peaking and carbon neutrality", this paper for the first time provides evidence of the effect of development zones on urban carbon emission performance; it does this by constructing a difference-in-difference (DID) model, which is based on panel data for Chinese cities in the period 1997-2017. The findings of the study indicate that the establishment of development zones had a positive influence on urban carbon emission performance without time lag effects. Our heterogeneity analysis of city groups indicated that development zones located in eastern region, or in large or coastal cities, exerted a more significant impact. The mechanism analysis that we conducted demonstrates that development zones promote carbon emissions performance by increasing GDP and reducing carbon emissions. Finally, this paper puts forward a series of policy measures, which are intended to strengthen the environmental benefit of development zones within the context of an urban green transition.

Evolution and control of the COVID-19 pandemic: A global perspective.

We investigated the factors influencing the progression of the pandemic from a global perspective by using the Geodetector and Correlation methods and explored the pandemic response policies and effects in different countries. The results yielded three notable findings. First, empirical results show the COVID-19 pandemic is influenced by various factors, including demographic and economic parameters, international travelers, urbanization ratio, urban population, etc. Among them, the correlation between urban population and confirmed cases is strongest. Cities become the key factor affecting the COVID-19 pandemic, with high urbanization levels and population mobility increases the risk of large-scale outbreaks. Second, among control measures, School-closures, International-travel-restrictions, and Public-gathering-restriction have the best control effect on the epidemic. In addition, the combination of different types of control measures is more effective in controlling the outbreak, especially for Public-gathering-restrictions ∩ School-closures, International-travel-restrictions ∩ Workplace-closures, Public-transport-restrictions ∩ International-travel-restrictions. Third, implementing appropriate control measures in the first month of an outbreak played a critical role in future pandemic trends. Since there are few local cases in this period and the control measures have an obvious effect.

Patterns and driving forces of the agricultural water footprint of Chinese cities.

The patterns and determinants of different types of agricultural water footprints in China are poorly understood at the prefecture-city level. In this paper, we evaluate Chinese agricultural water footprints from 2000 to 2017 and analyzed their spatio-temporal characteristics. Our estimation results show that the annual average agricultural water footprint in China was 5.038 × 109 m3, and the proportions of green water, blue water, and gray water were 70%, 9%, and 21%, respectively. In addition, high agricultural water-footprint cities with obvious urban agglomeration effects are mainly located in the Northeast, the Huanghuai River, the Yangtze River Basin, and Northwestern of Xinjiang, while low agricultural water-footprint cities are concentrated in high coastal urbanization-level areas or less developed agricultural areas of the west. We also investigate their determinants using a spatio-temporal fixed-effect model and find that GDP per capita, total investment in fixed assets, the income level of rural residents, the proportion of food grown, spray and drip irrigation technology, low-pressure pipe irrigation technology and seepage control irrigation technology have significant positive impacts on the agricultural water footprint. In contrast, the proportion of secondary and tertiary industries, social retail consumption, urbanization, technology expenditure, and the effective irrigation area proportion have a significant inhibitory effect. The primary determinants of the agricultural water footprint also vary substantially across water footprint categories (green, blue, and gray water footprints) and regions. Our findings imply that the agricultural water footprint should be incorporated into city water resource management and monitoring system.

Spatiotemporal patterns of global carbon intensities and their driving forces.

Carbon intensity (CI) is a valuable indicator of the balances struck by the world's governments between economic growth and environmental issues. This study investigates spatiotemporal variations in the CI levels related to energy consumption, as well as the spatial heterogeneity of its driving forces, in 208 countries globally during 2000-2018. To do this, we obtained data from the International Energy Agency (IEA) and the World Bank, employing methods of exploratory spatial data analysis (ESDA) and standard deviation ellipse (SDE) in order to analyze CI's spatiotemporal variations. We also performed a geographically weighted regression (GWR) analysis to determine the spatial heterogeneity of CI and the strength of its influencing factors. Our results reveal that: (1) Carbon emissions from energy consumption increased, while CI decreased globally, with the CI of most countries and regions declining significantly. (2) Global CI evidenced a heterogeneous spatial distribution, with higher-value areas concentrated in Asia and lower-value areas in Africa and Western Europe; obvious spatial agglomeration was also presented, especially with respect to High-High and Low-Low agglomerations, and the gravity center point moved from the northeast to the southwest. (3) The 8 influencing factors investigated in this study all had effective explanatory power in relation to CI globally. These factors showed significant spatial heterogeneity, and energy structure was the only factor to have a fully positive influence on CI, while foreign direct investment, foreign trade openness, industrial structure, total population, and energy intensity, mainly exerted a positive influence, and the urbanization rate and GDP per capita exerted a negative influence. By clarifying the spatiotemporal variations characteristics of global CI and the spatial heterogeneity of its influencing factors, this study provides a targeted reference for reducing CI and promoting sustainable development, globally.

A global North-South division line for portraying urban development.

Rapid urbanization has tremendously changed the global landscape with profound impacts on our society. Nighttime light (NTL) data can provide valuable information about human activities and socioeconomic conditions thus has become an effective proxy to measure urban development. By using NTL-derived urban measures from 1992 to 2018, we analyzed the spatiotemporal patterns of global urban development from country to region to city scales, which presented a distinct North-South divergence characterized by the rising and declining patterns. A global North-South division line was identified to partition the globe into the Line-North and the Line-South geographically, which accorded with the socioeconomic difference from the aspects of urban population and economy. This line may keep a certain degree of stability deriving from the trends of population and economic information but also bears uncertainties in the long term.

The varying driving forces of urban land expansion in China: Insights from a spatial-temporal analysis.

The impacts of socioeconomic development on urban land expansion in China vary across space and time; however, comprehensive investigation of this issue remains scarce in the existing literature. This study used a geographically and temporally weighted regression model (GTWR) to examine the spatiotemporally heterogeneous impacts of socioeconomic factors on urban land expansion in China using a newly available annual urban land-use dataset from 2000 to 2015. We found that although the eastern region has maintained its leading role (53.79%) in terms of urban expansion, the share of the central (20.34%) and western (16.13%) regions is gradually increasing. Cities with a higher administrative status tended to expand more rapidly; however, increasingly expansion has also taken place in the prefecture-level cities in recent years. We further found that Gross domestic product (GDP) growth, population density, and capital investment positively contributed to the expansion, although the directions and strengths of association between these factors and urban expansion varied across space and time. Industrial structure and foreign direct investment (FDI) showed a similar variation change trend, with the number of cities evidencing a negative relationship rapidly expanding and increasingly being seen not just in northwest China but also in the southeast during the study period. We also found that the correlation between public finance expenditure and urban expansion presented significant north-south differentiation. It is worth noting that governmental behavior plays a significant role in driving urban land expansion. Our empirical study confirmed the spatiotemporal heterogeneous effects of socioeconomic factors on urban expansion in China, providing useful insights for city governments and urban planners.

The varying driving forces of PM2.5 concentrations in Chinese cities: Insights from a geographically and temporally weighted regression model.

BACKGROUND: Particulate pollution is currently regarded as a severe environmental problem, which is intimately linked to reductions in air quality and human health, as well as global climate change. OBJECTIVE: Accurately identifying the key factors that drive air pollution is of great significance. The temporal and spatial heterogeneity of such factors is seldom taken into account in the existing literature. METHOD: In this study, we adopted a geographically and temporally weighted regression model (GTWR) to explore the direction and strength of the influences of natural conditions and socioeconomic issues on the occurrence of PM2.5 pollutions in 287 Chinese cities covering the period 1998 to 2015. RESULT: Cities with serious PM2.5 pollution were discovered to mainly be situated in northern China, whilst cities with less pollution were shown to be located in southern China. Higher temperature and wind speed were found to be able to alleviate air pollution in the country's southeast, where enhanced precipitation was also shown to reduce PM2.5 concentrations; whilst in southern and central and western regions, precipitation and PM2.5 concentrations were positively correlated. Increased relative humidity was found to reinforce PM2.5 concentration in southwest and northeast China. Furthermore, per capita GDP and population density were shown to intensify PM2.5 concentrations in northwest China, inversely, they imposed a substantial adverse effect on PM2.5 concentration levels in other areas. The amount of urban built-up area was more positively associated with PM2.5 concentration levels in southeastern cities than in other cities in China. CONCLUSION: PM2.5 concentrations conformed to a series of stages and demonstrated distinct spatial differences in China. The associations between PM2.5 concentration levels and their determinants exhibit obvious spatial heterogeneity. The findings of this paper provide detailed support for regions to formulate targeted emission mitigation policies.

Examining the effects of education level inequality on energy consumption: Evidence from Guangdong Province.

The dramatic economic growth and urbanization witnessed in China have been accompanied by a range of social and environmental problems. To comprehensively understand the influence of social inequality on climate change, the study analyzed the mechanism and pathways of the effects of education level on energy consumption, selecting typical indicators to reflect (i) the education quality within the population and (ii) the level of education development in order to evaluate the impact of education inequality and disparity. Under the framework of a STIRPAT model hypothesis, we investigated how the education level in Guangdong Province influenced energy consumption, using panel data from 2002 to 2017 and making a distinction between the Pearl River Delta region and the "non-Pearl River Delta" region. The empirical results show a significant disparity between the education level and energy consumption of the two regions. The education level has exerted significant effects in relation to energy consumption in the whole of Guangdong province. In cities with lower education levels, this impact was more obvious. However, in places with higher levels of education, this impact was overshadowed by other more significant factors, such as income level. The quality of the education within the population was found to increase energy consumption in the non-Pearl River Delta region, while this did not significantly impact on energy consumption within the Pearl River Delta. Our results hold implications for policy makers that they should adopt education methods and interventions to promote low-carbon knowledge and awareness that reflect the different stages of education development of regions. In this way, residents can be encouraged to develop low-carbon lifestyles, thereby reducing energy consumption and mitigating CO2 emissions.

Dynamics, differences, influencing factors of eco-efficiency in China: A spatiotemporal perspective analysis.

Eco-efficiency can effectively measure the relationship between economy, resources, environment, and development. Understanding eco-efficiency is of great practical significance for decision makers tasked with addressing and delivering sustainable socioeconomic development. Based on an "undesirable output Slacks-Based Measure models," this study evaluated the eco-efficiency of 285 Chinese cities during the period 2004-2014, analyzing spatiotemporal dynamics and influencing factors using a Spatial Autocorrelation Panel Data Model. The main results are as follows: From the spatial perspective, eco-efficiency in Chinese cities generally evidenced an M-shaped trend (increasing-decreasing-increasing-decreasing), and imbalanced spatiotemporal dynamics. Furthermore, the urban eco-efficiency generally presented a distinct convergence of HH cluster and LL cluster clubs, with the latter being the most dominant. From the regional point of view, there existed a decreasing trend in the efficiencies of cities, from eastern China to central and western China. In addition, we identified significant differences in the eco-efficiency of different cities in China, and the coefficient of variation of eco-efficiency showed a general decrease. The results of our estimation of the factors affecting urban eco-efficiency showed that the economic development level, the industrial structure, import and export trade, and the information level all had significant positive influence, and local government expenditure, social retail sales of consumer goods, and infrastructure all had a negative effect on urban eco-efficiency. This paper puts forward numbers of suggestions for ways to promote social, ecological, and economic development in Chinese cities, based on our findings.

How global value chain participation affects China's energy intensity.

China is currently the world's largest energy consumer and, at the same time, a huge trading power. With the increasing complexity of production processes along global value chains (GVCs), it is of great significance to study the impact of GVC participation on energy intensity. By using production length to measure GVC participation, this study first calculates China's manufacturing industry's total average production length of GVC activities and its three segments: length of pure domestic production, length of traditional trade production, and production length of GVC activities. Next, this study explains the influence mechanisms of GVC participation on energy intensity, proposes three research hypotheses, and conducts econometric analyses to examine the influence of production length and its three segments on energy intensity for a sample of China's manufacturing sector from 2000 to 2014. The results indicate that the total average production length of GVC activities significantly affects energy intensity and presents an inverted U-shaped, non-linear relationship wherein China has passed the critical point. The interactions between the three segments of production length and energy intensity also present an inverted U-shaped, non-linear relationship, where the impacts of the pure domestic segment and the "traditional" trade related segment on energy intensity have passed the critical point, while that of the segment related to GVC production has not yet crossed the critical point. It is suggested that further promoting China's manufacturing industry towards the mid-to-high end of the GVCs and breaking through its captive and "squeezed" position in the GVCs could significantly contribute toward curtailing energy intensity.

Emission reduction target, complexity and industrial performance.

To tackle climate change, Chinese government has applied an array of mitigating measures to reduce CO2 emissions. During the 11th Five-year Plan, Chinese government set the emission reduction target policy to reduce energy consumption per unit of gross domestic product (GDP) by 20%. This paper attempts to estimate the effect of this emission reduction target policy on industrial performance from complexity perspective. The result shows more complex industrial structure is related to less coal consumption. In general, emission reduction target policy has a negative effect on the probability of branching into new industries and lower the productivity and profitability of a given industry. However, this negative effect is weaker for more complex industries. Only for industries with very high complexity, emission reduction target policy can improve the performance of an industry rather than exerting a negative effect. Our finding not only helps to design a more effective policy to achieve industrial development strategy, but also provides a potential way to achieve economic growth while reduce the emission of greenhouse gases at the same time.

Global protected areas boost the carbon sequestration capacity: Evidences from econometric causal analysis.

Carbon sequestration capacity is the key factor in maintaining biodiversity and ecosystem services. However, further research is required on how to evaluate the impact of protected areas on carbon sequestration capacity from a global scale. To date, we propose a carbon density index of global protected areas (>10 km2, 32,756 samples) by the Integrated Valuation of Ecosystem Services and Trad'eoffs carbon model over the period 1994-2015. Then, we use the propensity score matching and difference-in-difference methods to separate the time effect and policy effect of the construction of protected areas on carbon sequestration capacity. Our analysis reveals that the carbon sequestration capacity can be improved by 0.39% by constructing global protected areas. There are regional differences with carbon sequestration capacity improvement globally. Africa has the largest value of increased carbon sequestration capacity, followed by Asia, Oceania and Europe. Upgrading protected areas (0.05%), strictly implementing planning (0.18%) and enhancing the power of local governments (0.08%) are conducive to improving carbon sequestration capacity. The assessment of the carbon sequestration capacity dynamic with protected areas is of great significance to meet the Convention on Biological Diversity.

Global urban expansion offsets climate-driven increases in terrestrial net primary productivity.

The global urbanization rate is accelerating; however, data limitations have far prevented robust estimations of either global urban expansion or its effects on terrestrial net primary productivity (NPP). Here, using a high resolution dataset of global land use/cover (GlobeLand30), we show that global urban areas expanded by an average of 5694 km2 per year between 2000 and 2010. The rapid urban expansion in the past decade has in turn reduced global terrestrial NPP, with a net loss of 22.4 Tg Carbon per year (Tg C year-1). Although small compared to total terrestrial NPP and fossil fuel carbon emissions worldwide, the urbanization-induced decrease in NPP offset 30% of the climate-driven increase (73.6 Tg C year-1) over the same period. Our findings highlight the urgent need for global strategies to address urban expansion, enhance natural carbon sinks, and increase agricultural productivity.

Does the path of technological progress matter in mitigating China's PM2.5 concentrations? Evidence from three urban agglomerations in China.

Technological progress is widely considered to play an important role in reducing air pollution. While growing literature has explored the effects of technological progress on environmental quality, fewer studies have considered the varied effects exerted by different technological progress paths on PM2.5 concentrations. This paper explored the relationship between two different kinds of technological progress (indigenous innovation and technology diffusion) and PM2.5 concentrations. Indigenous innovation was in this study considered to be composed by research and development investment (R&D) and import technology (IM); technology diffusion was represented by foreign direction investment (FDI) and export learning effect represent (EX). A panel data model was employed in order to explore the varied impact of these different technological progress paths on PM2.5 concentrations, using data for 48 cities located in China's three most developed urban agglomerations (Beijing-Tianjin-Hebei, the Yangtze River Delta, and the Pearl River Delta), for the period 2000-2015. The results reveal that without control variables, FDI had a significant negative impact on PM2.5 levels in Beijing-Tianjin-Hebei, and EX a significant positive impact. FDI, R&D, and EX were found to positively correlate with PM2.5 levels in the Yangtze River Delta. In Pearl River Delta, R&D presented a significant negative relationship. The findings of this study provide decision makers and industry managers with a scientific basis from which to approach the task of mitigating PM2.5 concentrations through technological progress.