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GDP growth of China, EU, USA, OECD and the world.

GDP growth of China, EU, USA, OECD and the world.

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Rapid urbanization consumes a variety of energy increasingly. The impacts of urbanization on energy consumption in the past decades have not been investigated by sectors in the literature. Using the time series energy and urbanization related data 1997–2016, this study aims to investigate the impacts of urbanization and its interaction with six ene...

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... the economic reform and open-door policies in 1978, China has experienced a rapid economic growth (Holz, 2008;Zheng et al., 2009;Perkins, 2012) and unprecedented urbanization process (He et al., 2017), compared with other developing and developed countries in the world (as shown in Fig. 1 and Fig. 2). As an important impetus of national economic growth ( Zhao and Wang, 2015), China's urbanization shows a large gap in urbanization level with developed countries, it is reasonable to forecast further increase in its urbanization level and impacts (Chang and Brada, 2006). The Chinese government has made strategic policies to promote ...

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... Research from the perspective of industrial development shows that productive EC in urban areas is significantly higher than that in rural areas (Li and Lin, 2015;Du et al., 2023), and changes in industrial structure at different stages of urbanization have different paths to EC (Mukhopadhyay and Forssell, 2005): the biggest influencing factor affecting China's urbanization process is the industrial structure (Lu, 1999), of which the secondary industry accounts for the largest proportion of total EC (Lv et al., 2019). With the development of urbanization, the ratio of light industry to heavy industry has changed significantly. ...
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Urbanization is not only a process of population transfer, but also a process of coordination and adaptation among population, space and industries, which can trigger multiple effects on energy consumption (EC). This study analyzed EC changes in China using a modified LMDI model. Most literatures indicated the industry production consumes amount of EC, but this paper unpacked the influencing factors in details, found that urbanization has a greater pulling effect on EC of production side over life side. Household consumption drives EC increase in production. The convergence of urban and rural consumption behaviors stimulates EC in daily life. Unreasonable land use intensifies EC, while population agglomeration affects EC fluctuation negatively. Upgrading industries and technology alleviate EC, and the consumption inhibiting effect has an inverted U-shaped effect. Hence, improving residents' consumption habits and optimizing spatial resource allocation are crucial for reducing EC while industrial development faces bottlenecks.
... In the summer of 2022, its total societal electricity demand surged beyond 110 million kilowatt hours, rivaling the consumption of South Korea in Asia and surpassing that of Germany, the largest industrial powerhouse in the European Union. China's vast energy demands and strained supply networks present an intricate dilemma [14]. The suboptimal efficiency in energy utilization not only engenders substantial wastage but also exacerbates issues of lagging industrial capacity and environmental degradation, particularly in rural and select urban pockets, thereby impeding China's march towards modernity [15]. ...
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As the world’s largest emerging market country, not only has China faced the contradiction between its huge population size and per capita energy scarcity for a long time, but the rigid constraints brought by energy poverty have also plagued the lives and production of Chinese residents. Based on panel data from 30 provinces (except Tibet) in mainland China from 2009 to 2021, this study employs double machine learning and spatial difference-in-difference for causal inference to explore the impact of a medium- to long-term regional innovation pilot policy in China—the new policy for innovative transformation in regional industrial chains—on energy poverty alleviation. This study also introduces China’s conversion of new and old kinetic energy into this quasi-natural experiment. This study presents the following findings: (1) The new policy for innovative transformation in regional industrial chains and the concept of the conversion of new and old kinetic energy can both significantly promote energy poverty alleviation. (2) The mechanism pathway of “the new policy for innovative transformation in regional industrial chains → the conversion of new and old kinetic energy → the energy poverty alleviation in heating/household electricity/transportation segments” has proved to be an effective practice in China. (3) Based on the spatial double difference model, the spatial direct effect of the new regional industrial chain innovation and change policy on energy poverty alleviation is significantly positive, while the spatial direct effect and spatial spillover effect of the new and old kinetic energy transformation on energy poverty alleviation are both significantly positive. (4) Based on the counterfactual framework analysis, in addition to the causal mediating mechanism of the demand-side conversion of new and old kinetic energy being impeded, both the supply-side and the structural-side conversion of new and old kinetic energy are able to play a significant positive causal mediating role in both the treatment and control groups.
... Urbanization, especially industrialization, contributes to increased energy usage. Moreover, urbanization leads to shifts in household energy consumption habits, including increased use of electrical appliances, contributing to higher energy consumption (Donglan et al., 2010;Lv et al., 2019). Secondly, household attributes significantly shape energy consumption patterns. ...
... To avoid the possibility of biased regression results due to the point estimates set by using the spatial regression model, this study adopts a partial differential approach to decompose the total effect (Lv et al. [26]). Then, the above equation can be converted into the following equation: ...
... The spatial spillover effect of PD on ULUE in large cities was greater than its direct effect. The promotion of land use efficiency by urban infrastructure construction is weak because the spatially fragmented agglomeration provides service support to the surrounding areas and consolidates the labor distribution, technology transfer, and information exchange functions of large cities, making the spatial spillover effect stronger (Lv et al. [26]; Zhang et al. [58]). Therefore, input factors such as capital and labor should be more concentrated in metropolitan areas and urban agglomerations, thus promoting higher land use efficiency (Peng et al. [29]; Song et al. [34]). ...
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The contradiction between the basin’s economic importance and its role as an ecological barrier impedes efficient urban land use. This study aims to propose an integrated approach to compare the urban land use of two representative basin areas of the Yangtze River Economic Belt and the Yellow River Basin and to investigate the impact of urban form on urban land use efficiency. Urban form was characterized by landscape indexes including Patch Density, Largest Patch Index, Edge Density, Patch Cohesion Index, and Agglomeration Index based on FRAGSTATS 4.0 software, and urban land use efficiency was measured by using Slack-Based Model-Undesirable, considering urban land becomes an emission source. Furthermore, spatial econometric models were adopted to explore direct effects and spatial spillover effects of urban form on urban land use efficiency. From 2000 to 2018, changes in urban form in both Yangtze River Economic Belt and Yellow River Basin showed increased fragmentation, enhanced heterogeneity, and more complex patch shapes. The high values of urban land use efficiency were concentrated in lower reaches of the Yangtze and Yellow Rivers. Spatial econometric models suggested that between different basins and various sized cities, the impact of urban form on urban land use efficiency had a spatial spillover effect and regional heterogeneity. Results indicated that input factors such as capital and labor should be more concentrated in metropolitan areas and urban agglomerations, thus promoting higher land use efficiency.
... China is a vast country, and there are robust differences in geographical spatial distribution, resource patterns, climatic conditions, industrial structure and economic development level, but there are also strong spatial correlations [3]. As an important driving force of economic development, energy consumption not only has absolute and conditional β convergence among provinces in China [4] but also obvious spatial dependence and agglomeration characteristics in geographical space [5]; that is, the energy consumption behavior of a region is affected by the energy consumption behavior of neighboring regions, and the degree of agglomeration continues to increase [6][7][8][9]. Many scholars have used exploratory spatial data analysis (ESDA) and spatial econometric models to reveal the spatial dependence of energy consumption. ...
... The higher the DC i value is, the more associations there are between province i and the other provinces in the network; that is, province i is in the center of the network. The degree centrality can be calculated according to Formula (6), where n is the number of provinces directly associated with the province, and N is the maximum possible number of connected provinces: ...
... Therefore, government fiscal expenditure is considered to be an important factor leading to an increase in energy consumption [18]. The government's energy-saving policies, public transport improvement, energy-saving lifestyle and other environmental regulatory policies may improve energy efficiency and reduce energy use [22]. However, these mitigation effects may be offset by economic growth and living demand. ...
... The relevant costs of economic activity exchange in regions with close geographical distance are relatively low, so energy consumption transfer between departments is more likely to occur in geographically adjacent regions [24]. Subsidies, taxes, energy conservation policies and other environmental regulatory policies adopted by the government may improve energy efficiency and reduce energy use [22]. With large-scale infrastructure construction, the continuous improvements in refrigeration, heating systems and public transport facilities, the energy demand may continue to grow [25]. ...
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Accurately understanding the correlation characteristics of energy consumption between regions is an important basis for scientifically formulating energy policies and an important entry point for realizing carbon peak and carbon neutrality goals. Based on the energy consumption data of the Yangtze River Delta urban agglomeration (YRDUA) from 2004 to 2017, the social network analysis method is applied to investigate the spatial correlation characteristics of the energy consumption of 26 cities and its influencing factors in the YRDUA. The energy consumption presents an obvious spatial correlation network structure. The network density fluctuates by approximately 0.3, and the network structure is relatively stable. Hangzhou, Suzhou and other cities are at the center of the network, playing the role of intermediaries. In the network, 10 cities, such as Shanghai and Shaoxing, have the characteristics of bidirectional spillover effects and act as “guides”, while Nanjing, Yangzhou and Chuzhou have the characteristics of brokers and act as “bridges”. The regional differences in geographical adjacency, FDI, industrial agglomeration and environmental regulation intensity are positively correlated with the network, and the impact coefficients are 0.486, 0.093, 0.072 and 0.068, respectively. Infrastructure differences are negatively correlated with the network, with an impact coefficient of −0.087.
... Spatial autocorrelation testing can be adapted to verify this assumption (Elhorst, 2014). The SAR model and SDM are adopted to discover the spillover effect of digital finance on industrial structure (Lv et al., 2019). ...
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Digital finance is playing an increasingly prominent role in economic development. This paper examines the impact of digital finance on industrial structure upgrading based on panel data from 289 Chinese prefecture-level cities from 2011 to 2020. The paper adopts fixed effects, mediating effects, and spatial econometric models and the findings are as follows. First, digital finance development significantly boosts industrial structure upgrading in Chinese cities. The evidence remains valid after various robustness tests. Second, digital finance and industrial structure upgrading exhibit positive spatial spillover effects. Third, digital finance indirectly affects industrial structure upgrading through innovation, entrepreneurship and the structure of household consumption channels. Fourth, the influence of digital finance is more significant in cities with more developed economies, less financialization and lower income inequality. Finally, among the sub-indicators of digital finance, the breadth of coverage plays the most significant role, inspiring policymakers and financial institutions to speed up the digitization infrastructure in backward areas.
... For example, Feng et al. (2018) used SDM model to study the impact of air pollution control on urban environment; Feng and Wang (2019), Jiang et al. (2019) and Lv et al. (2019) used SDM to study the correlation between urbanization level and haze pollution and energy consumption; Long et al. (2020) and Xie et al. (2020) studied green finance in the Yangtze River economic belt; In addition to spatial autocorrelation, more SDM models have been used to study the spillover effect of air quality, such as the study of PM2.5 spillover effect on air quality improvement by Tong et al. (2020), the study of the impact of air pollution spillover effect on public health by Chen et al. (2016), and the study of carbon emission spillover effect of Bohai rim economic circle by combining Moran's I with SDM by Song et al. (2020). Wu and Pu (2020) and Yang and Xu (2020) studied the impact of air pollution on income level through SDM. ...
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With rapid population growth and productivity development, the contradiction between economic and social development and resource and environmental protection is becoming increasingly prominent, so it is important to study the regional environmental carrying capacity to protect the environment and promote high-quality economic development. This study takes the three major urban agglomerations of Beijing-Tianjin-Hebei, Yangtze River Delta and Pearl River Delta as the research objects, and uses the entropy-weighted topsis model and the obstacle diagnosis model to evaluate the comprehensive environmental carrying capacity levels of the three major urban agglomerations and analyse their main obstacle factors. The results show that: 1) the average environmental carrying capacity level scores of the three major urban agglomerations during the study period were Beijing-Tianjin-Hebei (0.23103) > Yangtze River Delta (0.17687) > Pearl River Delta (0.16); 2) the degree of synergy between subsystems affects the environmental carrying capacity level; 3) China has still not achieved harmony between the environment and economic growth, and economic development is the main influencing factor for the level of environmental carrying capacity. In the future, it is recommended that each city cluster adhere to the construction of ecological civilization and vigorously develop high-tech and green industries; at the same time, give full play to the radiation-driven role of the regional core cities, make use of the synergistic effect of resource agglomeration and maximize the efficiency of resource utilization, so as to ultimately achieve the coordinated development of economic society and regional resources and environment.
... Research on consumption spending is an important issue, but it has been mostly analyzed by traditional econometric models, such as the panel data model and even less by the spatial econometric model. A review of past studies using spatial econometric models runs as follows (Bao and Chen 2017;Filippini et al. 2009;Funashima and Ohtsuka 2019;Lv et al. 2019). For example, Funashima and Ohtsuka (2019) explored the spatial crowding-out and crowding-in effects of government expenditure on Japan's private sector. ...
... They argued that policymakers should consider spatial spillovers and regional differences, as well as boost regional economies by stimulating private demand. Lv et al. (2019) discussed the determinants of the impact of urbanization on energy consumption and whether the growth of energy consumption in one province in China has a demonstration effect and spillover effect on surrounding areas. Bao and Chen (2017) studied the influencing factors of water consumption efficiency in China and mentioned that the water consumption efficiency of different provinces has significant spatial autocorrelation characteristics. ...
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The main purpose of this study is to explore the determinants of average household consumption spending in counties and cities from the two aspects of government fiscal expenditure and household characteristics. A spatial econometric model, the spatial Durbin model, was used to analyze Taiwan’s county-level and municipal panel data from 2000 to 2020. Global spatial autocorrelation and local spatial autocorrelation were applied to examine the overall degree of spatial agglomeration of average household consumption spending in Taiwan and the agglomeration status of specific counties and cities. The empirical results show that the average consumption spending per household of all counties and cities in Taiwan presents spatial autocorrelation, and the agglomeration of specific counties and cities is affected by different ruling parties of the central government. In terms of direct effects, the average consumption spending per household in local counties and cities is influenced by household characteristics, including average disposable income per household, average number of employees per household, and average living area per capita. In terms of the spatial spillover effect, the average consumption spending per household in local counties and cities is influenced by household characteristics of the neighboring counties and cities, including the average disposable income per household and the average living area per capita. Surprisingly, local economic development expenditure and local expenditure on education, science, and culture have no significant impact on the average consumption spending per household in counties and cities. The results of this study can be taken as a reference for government policymaking.
... For example, Feng et al. (2018) used SDM model to study the impact of air pollution control on urban environment; Feng and Wang (2019), Jiang et al. (2019) and Lv et al. (2019) used SDM to study the correlation between urbanization level and haze pollution and energy consumption; Long et al. (2020) and Xie et al. (2020) studied green finance in the Yangtze River economic belt; In addition to spatial autocorrelation, more SDM models have been used to study the spillover effect of air quality, such as the study of PM2.5 spillover effect on air quality improvement by Tong et al. (2020), the study of the impact of air pollution spillover effect on public health by Chen et al. (2016), and the study of carbon emission spillover effect of Bohai rim economic circle by combining Moran's I with SDM by Song et al. (2020). Wu and Pu (2020) and Yang and Xu (2020) studied the impact of air pollution on income level through SDM. ...
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Beijing-Tianjin-Hebei urban agglomeration (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) are the most important economic hinterlands in China, offering high levels of economic development. In 2020, their proportion of China’s total GDP reached 39.28%. Over the 5 years of 2014–2018, the annual maximum air quality index (AQI) of the three major urban agglomerations was greater than 100, thus maintaining a grade III light pollution (100 < AQI < 200) in Chinese air standards. This research thus uses a two-stage empirical analysis method to explore the spatial-temporal dispersal physiognomies and spillover effects of air quality in these three major urban agglomerations. In the first stage, the Kriging interpolation method regionally estimates and displays the air quality monitoring sampling data. The results show that the air quality of these three major urban agglomerations is generally good from 2014 to 2018, the area of good air is gradually expanding, the AQI value is constantly decreasing, the air pollution of YRD is shifting from southeast to northwest, and the air pollution of PRD is increasing. The dyeing industry shows a trend of concentration from northwest to south-central. In the second stage, Moran’s I and Spatial Durbin Model (SDM) explore the spatial autocorrelation and spillover effects of air quality related variables. The results show that Moran’s I values in the spatial autocorrelation analysis all pass the significance test. Moreover, public transport, per capita GDP, science and technology expenditure, and the vegetation index all have a significant influence on the spatial dispersal of air quality in the three urban agglomerations, among which the direct effect of public transport and the indirect effect and total effect of the vegetation index are the most significant. Therefore, the China’s three major urban agglomerations (TMUA) ought to adjust the industrial structure, regional coordinated development, and clean technology innovation.