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

Abstract

Residential carbon dioxide emissions can be divided into a direct component caused by consumers via direct energy usage and an indirect component caused by consumers buying and using products to meet their needs, with a higher proportion caused by the latter. Based on Beijing panel data for 1993–2012, an economic boom period in China, indirect carbon dioxide emissions were separately calculated for urban and rural households using the consumer lifestyle approach (CLA) model. Then, an extended stochastic impact by regression on population, affluence, and technology (STIRPAT) model was used to analyze the influence from two aspects, social economy, and land use, with high precision. Results indicate that indirect CO2 emissions in Beijing households display a rising trend in urban areas but a slight decrease in rural areas. Technology influences and forest land are, respectively, the most important aspects of the social economy and land use. Higher population and urbanization resulted in enhanced emissions in both urban and rural areas. The Engel coefficient presented a negative correlation with indirect CO2 emissions for both rural and urban areas. Compared with urban areas, the per capita net income of rural areas restrained consumption. The consumption structure of urban residents was more biased toward the tertiary industry than that of rural residents. Although technical progress has proceeded, it cannot offset urban residents’ indirect CO2 emissions caused by the large amount and rapid growth of consumption. Regarding land use, urban construction land net primary productivity (NPP) was high and not an important factor contributing to indirect CO2 emissions. Forest and lawn primarily served a recreational function and exhibited a positive impact. Water and cultivated land offered insufficient production and thus had a negative influence. For rural residents, lawn and cultivated land production is self-sufficient. Forests offer a carbon sequence effect, and construction land expansion increased the proportion of developed area, offering a scale effect that resulted in reduced carbon emissions. Based on the results, alternative carbon emission reduction policies have been proposed for each tested influence aspect to reduce emissions, including policies for optimizing industrialization quality, constructing a medium-density city, increasing space efficiency, encouraging sustainable consumption behavior, and increasing the efficiency of energy utilization.

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

“…Only six driving factors affecting the direct HCEs in Central China were explored, specifically energy structure, per capita consumption expenditure, energy price, energy demand, population urban-rural structure, and population size. However, direct HCEs may be influenced by the number of households [48], temperature, Engel coefficient [30], demographic structure [48], and other socioeconomic factors. Thus, in our future work, these factors to the direct HCEs should be given greater attention.…”

“…These methods can be classified into four major types [25,26]: emission coefficient method (ECM), consumer lifestyle approach (CLA), life-cycle assessment (LCA), and input-output analysis (IOA). ECM is widely adopted to measure the direct HCEs [27,28], while CLA [11,29,30], IOA [8,31,32], and LCA [33] are mostly used to calculate the indirect HCEs. In the existing literatures on energy consumption from the household sector of China, scholars have analyzed the HCEs in terms of their urban-rural or regional differences.…”

“…Yin et al [46], Yang et al [27], and Wang et al [35] used a similar method to explore the influencing factors of HCEs in Beijing, Jiangxi, and Guangdong, respectively. Moreover, more and more scholars have used the STIRPAT model to analyze the impact factors of HCEs in recent years, such as Jin et al [30], Wang et al [47], Zhu et al [48], Miao et al [49], and Wang et al [50]. The STIRPAT model is more extensible, and more socioeconomic factors can be incorporated.…”

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

“…At the city level, the factors that influenced indirect emissions in the household were studied in Beijing, and urbanization was one of those that increased them [54]. In addition, 49 cities were studied in Japan using density as a variable.…”

Supply-demand bilateral energy structure optimization and carbon emission reduction in Shandong rural areas based on long-range energy alternatives planning model

“…Many studies in literature have focused on demand-side or supply-side optimization of energy for rural residents using different models. As for the rural energy demand side, the researches mainly concentrated on energy consumption characteristics (Wu et al, 2019;Zou and Luo, 2019;Yang et al, 2021) and optimization of consumption structure (Jin et al, 2019;Hong, 2020;Wang et al, 2021;Imran et al, 2022). Zou et al (Zou and Luo, 2019) and Wu et al (Wu et al, 2019) proved that the current energy consumption of rural residents was dominated by biomass energy, and could be transformed to clean energy in the future.…”