Factors Controlling Urban and Rural Indirect Carbon Dioxide Emissions in Household Consumption: A Case Study in Beijing

Spatial and Temporal Characteristics and Drivers of Agricultural Carbon Emissions in Jiangsu Province, China

Scientific measurement and analysis of the spatial and temporal distribution characteristics of agricultural carbon emissions (ACEs) and the influencing factors are important prerequisites for the formulation of reasonable ACEs reduction policies. Compared with previous studies, this paper fully considers the heterogeneity of rice carbon emission coefficients, measures and analyzes the spatial and temporal characteristics of ACEs in Jiangsu Province from three carbon sources, including agricultural land use, rice cultivation, and livestock and poultry breeding, and explores spatial clustering patterns and driving factors, which can provide a reference for agricultural low-carbon production. The results indicate that from 2005 to 2020, Jiangsu's ACEs showed a decreasing trend, with an average annual decrease of 0.32%, while agricultural carbon emission density (ACED) showed an increasing trend, with an average annual increase of 0.16%. The area with the highest values for ACEs is concentrated in the northern region of Jiangsu, while the areas with the highest values for ACED are distributed in the southern region. The spatial clustering characteristics of ACEs have been strengthening. The "H-H" agglomeration is mainly concentrated in Lianyungang and Suqian, while the "L-L" agglomeration is concentrated in Zhenjiang, Changzhou, and Wuxi. Each 1% change in rural population, economic development level, agricultural technology factors, agricultural industry structure, urbanization level, rural investment, and per capita disposable income of farmers causes changes of 0.112%, -0.127%, -0.116%, 0.192%, -0.110%, -0.114%, and -0.123% in Jiangsu's ACEs, respectively. To promote carbon emission reduction in agriculture in Jiangsu Province, we should actively promote the development of regional synergistic carbon reduction, accelerate the construction of new urbanization, and guide the coordinated development of agriculture, forestry, animal husbandry, and fisheries industries.

Which Factors Influence the Regional Difference of Urban–Rural Residential CO2 Emissions? A Case Study by Cross-Regional Panel Analysis in China

Residential CO2 emissions (RCE) are an important component of anthropogenic CO2 emissions. In order to formulate reasonable reduction policies and measures, it is necessary to understand the dynamics of RCE production and the factors that influence it. This paper explores the spatiotemporal evolution characteristics of RCE from the perspectives of eastern, central, and western China, as well as urban–rural areas, and analyzes the factors driving RCE based on the STIRPAT model. The results indicated that between 2010 and 2019 the eastern region contributed the greatest proportion of overall RCE and the central region saw the largest increase. Per capita RCE in urban areas was greater than in rural areas, except in eastern China. The factors affecting RCE showed regional and urban–rural differences. Population size and income per capita were two dominant factors affecting RCE for all regions. The per capita income plays a more important role in developed regions such as eastern regions and eastern urban areas. Urbanization contributed to RCE reduction in the eastern region but promoted RCE in the central and western regions. Energy structure shows a negative effect on RCE, particularly in central urban and western rural areas. Appropriate policies and measures need to be put forward to local conditions. Reduction policies and measures of each region and urban–rural area need to be based on the dominant factors driving CO2 emissions.

Spatio-Temporal Differentiation Characteristics and Urbanization Factors of Urban Household Carbon Emissions in China

This paper explores the spatial relationship between urbanization and urban household carbon emissions at the prefectural level and above cities in China and uses Exploratory Spatial Data Analysis (ESDA) and Geographically Weighted Regression (GWR) to reveal the extent of the impact of urbanization on urban household carbon emissions and the spatial and temporal variation characteristics. The results show that: Overall carbon emissions of urban households in cities of China showed a decreasing trend during the study period, but there were significant differences in the carbon emissions of urban households in the four major regions. In terms of the spatial and temporal characteristics of urban household carbon emissions, the urban “head effect” of urban household carbon emissions is obvious. The high-high clustering of urban household carbon emissions is characterized by a huge triangular spatial distribution of “Beijing-Tianjin-Hebei, Chengdu-Chongqing, and Shanghai”. The level of urbanization in Chinese cities at the prefecture level and above shows a spatial pattern of decreasing levels of urbanization in the east, middle, and west. The four subsystems of urbanization are positively correlated with urban household carbon emissions in the same direction. The urbanization factors have a contributory effect on some cities’ carbon emissions of urban households, but there are significant regional differences in the impact of urbanization factors on urban household carbon emissions in the eastern, central, and western regions of China, as they are at different stages of rapid urbanization development.

Influence of Population Density on CO2 Emissions Eliminating the Influence of Climate

More than 50% of the world’s population lives in cities. Its buildings consume more than a third of the energy and generate 40% of the emissions. This makes cities in general and their buildings in particular priority points of attention for policymakers and utilities. This paper uses population density as a variable to know its influence on energy consumption and emissions produced in buildings. Furthermore, to show its effect more clearly, the influence of the climate was eliminated. The usual energy consumption in buildings is thermal and electrical. The study was carried out at the city level, both per inhabitant and per household. The area actually occupied by the city was considered. The proposed method was applied to the case of Spanish cities with more than 50,000 inhabitants. The results show that the higher the population density, the higher the energy consumption per inhabitant and household in buildings. The consumption of thermal energy is elastic, while that of electrical energy is inelastic, varying more than 100% between extreme groups. Regarding CO2 emissions, the higher the population density, the higher the emissions. Emissions of electrical origin barely vary by 2% and are greater than those of thermal origin. In addition, the proportion of emissions of electrical origin, with respect to the total, decreases with increasing population density from 74% to 55%. This research aims to help policymakers and utilities to take the appropriate measures that favor the use of renewable energies and reduce CO2 emissions.

Multi-Perspective Analysis of Household Carbon Dioxide Emissions from Direct Energy Consumption by the Methods of Logarithmic Mean Divisia Index and σ Convergence in Central China

Currently, household carbon dioxide (CO2) emissions (HCEs) as one of the leading sources of greenhouse gas (GHG) have drawn notable scholarly concern. Thus, here, taking six provinces in the period of 2000–2017 of Central China as a case, we analyzed the characteristics and the driving factors of HCEs from direct energy consumption and three perspectives: Central China as a whole, urban-rural differences, and inter-provincial comparison. The drivers of direct HCEs were analyzed by the Logarithmic Mean Divisia Index (LMDI). The σ convergence was adopted for analyzing the trend of inter-provincial differences on the HCEs. The key findings are as follows. First, all the direct HCEs from three perspectives had an obvious growth trend. The total direct HCEs grew from 9596.20 × 104 tonnes in 2000 to 30,318.35 × 104 tonnes in 2017, with an increase of 2.16 times. Electricity and coal use were the primary sources. The urban and rural increases of direct HCEs were up 2.57 times and 1.77 times, respectively. The urban-rural gap of direct HCEs narrowed first and then widened. The direct HCEs in the six provinces varied significantly, but the gap was narrowing. Second, as a whole the per capita consumption expenditure and energy demand were the main drivers to the increment of HCEs, with cumulative contribution rates of 118.19% and 59.90%. The energy price effect was mainly responsible for the mitigation of HCEs. Third, the similar drivers’ trend can also be seen from the perspective of inter-provincial comparison. However, from the perspective of urban and rural difference, the population urban-rural structure effect played a reverse influence on both urban and rural areas. Thus, raising the energy prices appropriately, upgrading the residents’ consumption to a sustainable pattern, controlling the growth of population size reasonably, and optimizing the household energy structure might effectively mitigate the growth of HCEs in Central China.

Impacts of Urbanization and Technology on Carbon Dioxide Emissions of Yangtze River Economic Belt at Two Stages: Based on an Extended STIRPAT Model

In the Yangtze River Economic Belt (YREB), one of the most important challenges at present is to promote green, low-carbon development. This study attempted to explore the impact of different dimensions of urbanization and technology on CO2 emissions at different stages in YREB by using an extended STIRPAT model on provincial panel data from 2000 to 2017. To examine the change differences based on the different effects of urbanization and technology on CO2 emissions, we divided the total study period into two stages according to the change trends of CO2 emissions and considered the YREB as a whole as well as the lower, middle, and upper reaches individually. The main findings are as follows. First, an inverted U relationship was found between economic urbanization and CO2 emissions for the entire study period along with the period of a rapid rise in CO2 emissions (Stage I) only in YREB and the upper reaches, while in the stable change period (Stage II), the inverted U relationship existed in the upper and lower reaches. An inverted U relationship between technology and CO2 emissions was only found in the middle reaches for Stage I and in the middle and lower reaches for Stage II. Second, during the entire study period, economic urbanization had the greatest inhibitory effect on carbon dioxide emissions, followed by energy intensity and population urbanization; during Stage I, the main reduction factors were economic urbanization and energy intensity, and population urbanization had a non-significant impact. Third, per capita gross domestic product (GDP) and population size had a positive impact on CO2 emission increases. Specifically, during Stage II, the fitting effect was not good (R2 is 0.3948), and the whole formula was not significant. In lower reaches, the economic urbanization had a positive impact at Stage I, the energy intensity had a rebound effect and per capita GDP had a non-significant impact at Stage II.

Empowering Sustainable Consumer Behavior in the EU by Consolidating the Roles of Waste Recycling and Energy Productivity

The growth in population and economic activities has direct implications on the deterioration of the natural capital, especially when referring to the increase in greenhouse gas emissions. However, improvement is possible by empowering sustainable consumption and production patterns. Through the 2030 Agenda for Sustainable Development, the United Nations call for a mix of economic development, environmental sustainability, and social inclusion. The agenda also provides the instruments needed to track progress, as each Sustainable Development Goal has a set of indicators meant to assess various dimensions of sustainability. Energy productivity is only one of many, but still special because it reflects sustainable consumption behaviors and production patterns. The 2030 Climate Target Plan elaborated by the European Commission consolidates and brings its contribution to the aims of the 2030 Agenda for Sustainable Development by adding weight to the importance of the greenhouse gas emissions target. The objective of this research was to study the relationship between energy productivity, greenhouse gas emissions, biowaste recycling and nominal GDP in the EU in order to highlight the key of a smooth transition towards sustainable consumption behaviors and production patterns. The results show that recycling, greening the economy and energy productivity are the vectors of this transition.