1. Introduction
With socio-economic development and a sharp increase in population, energy consumption is increasing worldwide. The geography of global energy consumption continues to shift toward countries experiencing rapid industrialization and urbanization, including China, which has already surpassed the United States as the largest emitter of carbon dioxide (CO
2) [
1]. With a relatively more centralized population and greater anthropogenic activity, metropolitan areas are major sources of global carbon emissions [
2,
3]. In particular, Beijing, the capital of China, with higher CO
2 emissions and per capita CO
2 emissions than metropolises in developed countries [
4], is becoming a common focus of carbon emission research [
5,
6,
7].
The period between 1993 and 2012 represents an essential stage for China, especially Beijing. During this period, Beijing experienced its fastest economic growth and realized its transition from an industry-oriented to a service-oriented structure. In 2012, Chinese economic development reached a ‘new normal’ stage, which is a stage of economic development in which different degrees of decline in the national economy growth rate occur [
8]. Before reaching this new normal stage, rapid urbanization and industrialization occurred in conjunction with relatively low efficiency and high energy use [
9]. Therefore, examining the energy consumption and related CO
2 emissions in Beijing from 1993 to 2012 will provide details of an essential historical experience for Beijing and provide a way to construct a low-carbon city, providing a reference and inspiration allowing other developing countries to realize energy conservation and emissions reduction goals.
Current studies of energy consumption and carbon emissions mainly focus on industry sectors [
10,
11]. More attention should be given to household carbon emissions, especially in China. Energy consumption in households in China in 2012 accounted for 11% of the total energy consumption, and the number was 25%, considering private traveling, according to the
Chinese Residential Energy Consumption Report (2014). Household energy consumption and related CO
2 emissions have been becoming indispensable components.
Residents’ lifestyle-relevant energy consumption and the related CO
2 emissions can be divided into a direct component caused by consumers using energy and an indirect component caused by consumers buying and using products to meet their basic needs [
12]. The indirect part of Chinese households is much higher than their direct consumption [
13,
14]. In addition, most research on indirect household carbon emissions remains at the national level, and there are few specific studies at the city scale [
15,
16,
17]. The existing studies have greatly assisted with energy conservation and emission reduction. However, because the many socioeconomic and environmental differences across regions in China increase the spatial heterogeneity of carbon emissions [
16,
18], research on indirect household carbon emissions at the city scale is necessary and has practical significance [
19]. Moreover, a better understanding of household energy consumption and carbon emissions at the city scale is necessary for Chinese decision-makers to address energy security and local pollution mitigation.
Urban areas contain 40% of the population and account for 75% of the Chinese national economy [
2]. Urban per capita energy consumption is typically 3.5 to 4 times greater than rural per capita energy consumption in China [
7,
20]. Considering the great difference between consumer lifestyles and the related indirect CO
2 emissions in China, urban and rural areas were separately studied in this paper.
Three estimation methods have been used to measure indirect household energy consumption: A hybrid energy analysis [
21], the family metabolic method, and the consumer lifestyle approach (CLA) method [
22]. Hybrid energy analysis and the family metabolic method treat consumption the item as a basic unit. They first calculate the energy consumption of each item and then add all consumption items up to get the household energy consumption and related emissions [
21]. Although these two methods provide relatively elaborate results, they require very detailed data. Moreover, these two methods are more suitable for micro-scale calculation, like a household or a community, but not for macro-scale, like estimating household CO
2 emissions in whole Beijing because the data volume would be significant and the calculation would be extremely complex. Compared with these two methods, the CLA method, which uses more accessible data, is commonly used to calculate indirect energy consumption and carbon emissions and was applied in this study [
23,
24].
Additionally, one of the keys to controlling CO
2 emissions is analyzing its influencing factors, and various methods have been used for this, including input–output models [
25], LEAP models and Urban-RAM models [
11], panel regression [
26], and Holos models [
27]. These models are primarily applied to analyses of the impact of macro-level factors, such as GDP, industry sector, and energy intensity They are province (or province city) level data and regard the urban and rural as a whole, thus it is not suitable for analyzing urban and rural separately [
28,
29]. The stochastic impact by regression on population, affluence, and technology (STIRPAT) model, another common factor decomposition model, was first applied in this area by York et al. [
30], and then broadly used based on panel or cross-sectional data [
16,
31,
32,
33]. In this study, the STIRPAT model was utilized.
Currently, although utilization of the STIRPAT model can resolve many problems, there are areas where its application could be improved. First, regarding the spatial and temporal scales, Brantley Liddle collected 28 articles based on the STIRPAT model at macroscopic scales and argued that the STIRPAT model should be applied to smaller spatial scales and longer time scales [
31]. Second, regarding the study area, recent studies have mainly focused on developed countries, while little attention has been given to developing countries [
31]. Finally, the STIRPAT model focuses less on changes in the extension of urban land use [
28,
34]. However, the process of urbanization not only involves a change in industrial structure and a shift in lifestyle but also includes transitions in the land-utilization. Urbanization leads to urban expansion into fertile and productive lands. This process not only directly decreases carbon storage but also releases more CO
2 [
35,
36], dramatically altering ecosystem functions and processes [
37,
38].
Net primary productivity (NPP), a sensitive indicator of the energy and material cycles of ecosystems [
39,
40], has been used to quantify the impact of land transformation on an ecosystem [
36,
41], determine the anthropogenic activities’ ecological impact, and reflect the impact of different lifestyles on the environment [
42]. Most of the existing research on the relationship between CO
2 emissions and NPP uses cross-sectional data, and studies analyzing inter-annual variations commonly use NPP of one year [
16], which leads to low precision. In this paper, based on the Carnegie–Ames–Stanford Approach (CASA) model and MODIS remote sensing data, the NPP of Beijing from 2001 to 2012 was calculated at a resolution of 250 meters by 250 meters to provide accurate results. Therefore, in addition to widely used social-economic factors [
34], the land-use factors based on the NPP were added to the analysis.
To address the aforementioned issues, first, Indirect CO2 emissions in urban and rural households in Beijing between 1993–2012, an economic boom period before ‘new normal’, are calculated. Second, land use influence factors are added to the widely used factors: Population, affluence, technology in STIRPAT analysis. Additionally, rather than using the annual data, net primary production is calculated using remote sensing data for each year to improve the accuracy of the results. Finally, policy suggestions are proposed. This research is expected to improve the analyses of the comprehensive influence of indirect CO2 emissions from households in other metropolises during rapid economic booms and the realization of energy conservation and emission reduction goals.
5. Conclusions and Recommendations
Residents’ indirect household CO
2 emissions fluctuate, showing a rising trend in urban areas but a small decrease in rural areas. The indirect CO
2 emissions of urban residents are much higher than that of rural residents, reflecting the differences between urban and rural residents’ consumption modes, with the former causing higher indirect CO
2 emissions. Compared to the factor analyzing results with studies in other regions, Beijing shows special characteristics. Studies in Finland and Thailand show urbanization is not strongly related with CO
2 emissions, and the indirect CO
2 emission produced by urban residents is slightly less than those of rural people [
15,
58]. As for the studies of different regions in China, the population was regarded as the primary driver, which is the same with rural Beijing [
58,
59]. For urban Beijing, technology and consumption structure shows greater influence. Moreover, secondary industry proportion showed a positive influence in current studies, however, it has a negative influence on indirect household CO
2 emission in urban Beijing [
5,
60]. Considering the dense population, significant urban-rural difference, and advanced consumption level, it is necessary to provide targeted carbon emission reduction policies.
Industrialization: The results showed that during the process of industrialization, the consumption of urban residents has a quicker reformation, i.e., the consumption structure of urban residents is more biased toward the tertiary industry than that of rural residents. Beijing, which has experienced a rapid economic boom, focused heavily on the pace of industrialization, resulting in a high ratio of tertiary industry output value but unbalanced and under-graded industrialization. Optimizing the industrialization quality will play an important role in increasing energy efficiency and realizing energy conservation and emissions reduction. For energy-intensive industries, the government should control the scale to prevent overcapacity and plan to phase them out. For the tertiary industry, the government should implement effective policies to encourage them to import materials. Moreover, the Chinese government should take advantage of the agglomeration effect to improve production efficiency, for example, encourage sectors to become specialized and build an inter-sector industrial chain to realize emission reduction.
Urbanization of population: Beijing, with its superior economic status and high degree of urbanization, similar to other metropolises around the world, has become a center of population aggregation for not only its rural residents but also immigrants from other provinces and even other countries. Aiming to become a low-carbon city, Beijing must reasonably control the population of permanent residents, especially the scale of its urban population, to improve the quality of urbanization and decrease population density.
Urbanization of land: As for urban residents’ indirect CO2 emissions, construction land NPP, which has been maintained at a high level, is not a crucial factor. Forests and lawns should be given priority given their recreational function and positive impact, and water and cultivated land show insufficient production, which has a negative influence. As for rural residents’ indirect CO2 emissions, lawn and cultivated land production can be self-sufficient. Forests provide a carbon sequestration effect, and construction land expansion reduces density, leading to carbon emissions reduction. Beijing urgently needs to formulate policies that design the appropriate scale and reasonable layout to increase energy use efficiency and realize indirect CO2 reductions. Beijing should focus on protecting forests and maintaining their rational distribution, especially in urban areas. Based on the result, the forest lands, especially those in urban areas, showed a significant positive role in emission reduction. Moreover, scientific management and clipping are important. The city should be constructed at a medium density to balance the use intensity of urban and rural construction land. The government can plan and provide more accessibility to the shared-used spaces and resources, making households less obligated to own everything and increase the efficiency of land utilization and frequency of the equipment usage.
Consumption behavior: The Engel coefficient presents a negative correlation with indirect CO
2 emissions for both urban and rural residents. Per capita net income presents a positive correlation with urban residents’ CO
2 emissions but a negative one with rural residents’ emissions. This result means that compared with urban residents, rural per capita net income and lifestyle have become an important constraint of rural residents’ consumption. On the one hand, considering the stimulus of economic development to household consumption, it is imperative for Beijing to encourage rational and green consumption. On the other hand, the government should always be alert to avoid disproportionately economic pressure caused by environmental protection on the lower-income rural residents and reduce inequality. Meanwhile, Beijing needs to intensify publicity to raise awareness of saving energy and protecting the environment. Studies have shown that raising awareness on environmental-friendly consumption has great potential to benefit sustainable development. Installation of energy and emission metering devices can provide the residents regular and effective feedback to change their behavior and live a sustainable life [
61].
Efficiency of energy utilization: Technology was proved to be the most important factor for indirect household CO2 emissions, within which, energy intensity has less of an effect and mechanical harvest area proportion has a greater positive influence for both urban and rural residents. The result suggested that Beijing should pay more attention to improving output per energy unit and cutting down the per-unit energy use. From the energy source perspective, China must formulate policies to increase the usage of renewable energy rather than high carbon coal, which accounted for nearly 66% of the total energy supply in 2012 (data from U.S. Energy Information Administration). From the energy consumption perspective, the government should encourage industrial intensivism. Especially for agricultural production, the government should encourage cooperation between farmers or villages, forming large-scale and intensive agricultural production, thus increase efficiency and reduce energy consumption.
Although this study contributes to a better understanding of urban and rural indirect CO2 household emissions in Beijing and their influence factor from socio-economic factors and land-use aspects during an economic boom period, it has some limitations. A future study should analyze CO2 emissions in the “new normal” stage, when different degrees of decline in the national economic growth rate occur. Studying and analyzing the CO2 emissions characteristics and how their influence factors work, have the potential to propose targeted policy recommendations.