1. Introduction
Targeted climate change mitigation policies can have co-benefits and have led to an interest in sub-national climate action. In particular, there is a growing emphasis on low-carbon city construction in developing countries. To achieve the low-carbon goal announced by the Paris Agreement, China should take different types of domestic low-carbon policies and actions into consideration. County is the basic administrative unit for the overall planning of Chinese urban and rural development. Different from city district, county is located in a more marginal area, and governs towns and villages. It usually has a lower level of industrialization and urbanization. However, it still plays a vital role in carbon control and emission reduction. In 2015, a total of 1929 counties, which covered 88% of the land area and contained 74% of the population, accounted for 60% of total carbon emissions. Carbon emission governance at the county level is very important for the achievement of low-carbon development goals. Compared with cities and regions, counties are smaller in size and face a more specific governance scenario. Most of the experiences of carbon emission governance in the typical large cities do not apply to the counties. Moreover, at the county level, there are great differences in carbon emission governance as a result of their wide differences between populations, distribution regions, and levels of urbanization. Therefore, at the county level, carbon emission governance needs to build a more detailed and systematic method of assessment for addressing more specific issues of low-carbon development. According to the systematic characteristics of low-carbon development issues, the counties should be classified into different zones as a basis on which a systematic and differentiated carbon emission governance path for Chinese counties can be constructed.
In the past decades, scholars from different disciplines have conducted a large number of carbon emission governance studies in different regions and on different spatial scales, and the studies mainly focus on the following three categories: low-carbon city indicators, governance methods, and case studies. First, a large number of studies on low-carbon indicators have been conducted on urban clusters [
1,
2,
3], large cities [
4,
5], and other large-scale objects, such as research on low-carbon urban assessment [
6,
7] and carbon emission estimation methods [
8,
9]. Second, studies on governance methods have proposed some carbon emission governance tools [
10,
11] or models [
12,
13,
14,
15], targeting one aspect, such as production or building, of governance, at the city level [
16,
17,
18]. Third, many carbon emission governance case studies have been conducted on small-scale objects, such as urban centers [
19], communities [
20,
21,
22], and industrial parks [
23,
24]. Also, more than 80 low-carbon pilots were set up in China from 2010 to 2017 and some research was conducted to analyze the carbon emission governance effects of the pilots. The studies attempted to realize the universal application of carbon emission governance through the governance method in a case-study city or the governance mode of a specific topic. To date, the research on carbon emission governance has focused on large scales, such as cities and regions, or some specific small-scale areas, and has provided many theoretical and methodological bases. However, the main drivers of carbon emissions in cities and counties are different because the overall levels of the development of the cities are higher than those of the counties whose levels of development are varied. It is indicated that the proposed low-carbon city indicators were not completely applicable to counties and that the policy tools and governance models for low-carbon cities could not work for all counties. To draw on the experiences of low-carbon cities, carbon emission governance for the counties requires further discussion considering the characteristics of the county.
On the county level, there are two difficulties in carbon emission governance. The large number of counties means that it is unrealistic to achieve targeted governance, such as pilot establishment. The huge differences in geographical locations and the levels of economic development among the counties have caused differences in the carbon emission drivers and have resulted in the consequence of applying uniform standards of low-carbon constraints in all counties during unified governance. However, based on preliminary estimates of the counties’ carbon emissions combined with the conclusions of some scholars on the counties’ economic and social development, this study found that the counties in the neighboring areas had similar levels of low-carbon development [
25]. It provides for the possibility of classified governance. For example, the average carbon emissions of the construction sector in counties north of the heating demarcation line (the north-south demarcation line, where the Chinese government subsidizes central heating in winter) were nearly twice those of the counties south of the line. The average industrial carbon emissions of the developed counties in the eastern region are nearly three times that of the less developed counties in the western region [
26].
Due to the above difficulties and findings, this study attempted to introduce the classification approach toward achieving differentiated carbon emission governance for counties. The specific goal of the study is to analyze the regional differences of the carbon driving factors and further establish a zoning system for carbon emission governance based on these factors. It will be helpful for the country to carry out macro-level regulation, and convenient for local governments to formulate specific and feasible low-carbon management measures based on local conditions, thereby reducing carbon emission in counties nationwide. To achieve the above goals, the study needs to solve the following problems: (1) identify the carbon emission governance elements in counties by formulating a cause-effect chain among the county’s level of development, carbon emission governance, and carbon emissions, and (2) classify all counties according to their performances on carbon emission governance elements in order to propose differentiated carbon emission governance frameworks for each type of county that would act as guides for policymakers and planners.
Considering the two problems above, methods for establishing a low-carbon evaluation indicator system and for zoning by geographical type have been extensively discussed. For the first method, scholars have mainly used literature inquisition [
27,
28], Triple Bottom Line, pressure-state-response (PSR) framework and its expansion framework, such as the driving force-pressure-impact-state-response (DPISR) framework [
29] and driving force-pressure-state-impact-response-management (DPSIRM) framework [
30], to formulate the indicator system. For example, Yang set up a three-tier low-carbon city evaluation indicator system with the PSR model and calculated the low-carbon city comprehensive score for Beijing in 2009 [
31]. Song and Li used the DPSIR model to build a low-carbon city indicator system and assessed the low-carbon development of the Yangtze River Delta [
32]. The PSR and its expansion framework have been widely used in research on city carbon emission governance [
33] and have proven advantages in building indicator systems and developing political goals related to environmental issues [
30]. For geographical zoning, the classical zoning methods can be categorized as clustering analysis [
34,
35,
36], spatial auto-correlation [
37,
38], and analytic hierarchy process (AHP) [
39,
40]. More suitable for governance-oriented zoning, clustering analysis is used mostly for multi-factor integrated zoning because it can reflect the differences and convergences among regions by integrating many governance elements. The zoning method is widely used in research on geographical zoning, such as geological zoning [
41,
42], climate zoning [
35,
43,
44], and ecological functional zoning [
45,
46,
47,
48], but rarely used in research on carbon emission governance. Moreover, most zoning research has focused on reflecting the external differences in the carbon emissions of regions directly [
49] but has disregarded the differences in the internal motivations. In general, both the PSR framework and clustering analysis have solid application foundations. The PSR framework is widely used to establish a carbon emission evaluation system, which can identify the cause-effect chain of governance indicators and carbon emissions to establish a carbon emission governance indicator system at the county level. Clustering analysis is used mainly in multi-factor comprehensive classification, which is suitable for carbon emission governance zoning. It is worth mentioning that the zoning in previous studies was mainly cognitive-oriented zoning rather than governance-oriented zoning. The zoning’s results do not reflect the internal differences in governance elements that could affect external differences in carbon emissions, so it was difficult to derive a governance strategy. In fact, governance elements such as population, land use, and facilities, can affect carbon emissions by affecting the energy consumption of the building and transportation sectors [
50,
51,
52,
53]. Therefore, to establish a zoning system oriented toward carbon emission governance, it is essential to find the effects of governance elements on carbon emissions. This study attempted to reflect the differences in the counties’ carbon emissions through the governance elements, then guide the counties’ low-carbon development by the governance elements. Finally, to derive differentiated governance standards, the current Code for County Management, which has set the standard thresholds for most governance elements, can be referenced. Although this document does not consider carbon emission governance, a comparison of its proposed standard scenarios and the realistic scenarios of the counties in different zones is of much relevance to clear, differentiated carbon emission governance targets for the counties.
The framework of “identifying the cause-effect chain among county development, carbon emission governance, and carbon emissions—establishing carbon emission governance indicator and carbon emission governance zoning systems for the counties—setting a differentiated governance system” consisted of the following steps (
Figure 1): (1) introduce the county governance elements into the PSR framework and identify the cause-effect chain so that the governance elements in the chain would be used to build the initial carbon emission governance indicator system, (2) further screen the initial governance indicators that do affect carbon emissions by analyzing their carbon effects to establish a modified indicator system, (3) collect data for the counties’ indicators and classify all the counties with the clustering analysis method to draw a carbon emission governance zoning map at the county level, and (4) reference the current Code for County Management, so that the differentiated governance system, including key governance indicators and the low-carbon target thresholds of the indicators, would be established for each zone.
This paper intends to construct a governance-oriented carbon emission zoning at the county level. This research will help policymakers develop differentiated and locally applicable strategies for controlling and reducing carbon emissions, which is important for sustainable development. The zoning presented in this paper can help to clarify the differences in carbon management among the counties and the directions of governance for the counties in different zones. The proposed differentiated zoning method is based on multi-indicator evaluation and optimizes the classical zoning method to establish governance-oriented zones that can reflect the differences in both external carbon emissions and internal motivations, as well as provide new ideas for the carbon emission governance of small-scale regions with large populations.
3. Results
3.1. Overall Analysis of Zoning Results
Following the differentiated zoning method mentioned above, 1753 counties were taken as the objects and the values of the 6 sectors were taken as the classification variables for clustering. When the number of clusters is 5, the classification results can reflect the differences in the governance problems among the zones relatively clearly and reasonably.
Table 4 lists the 5 zones and some of the included counties.
In every zone, the average and total value of each sector of the counties were calculated to reflect the zones’ carbon emission governance problems in each sector and the overall level of low-carbon development. The higher the average value of the sector, the more critical is the governance problem in it. For a zone as a whole, a high total value means relatively high carbon emissions. The table in
Figure 3 shows the average and total values of the 5 zones in the 6 sectors.
There are wide gaps between the total value and the coverage area of the 5 zones. The first zone, whose total value is significantly higher than the other districts, is identified as a high-carbon governance zone. It covers 22.25% of the counties. The second and third zones, with a medium total value, are identified as medium-carbon governance zones, covering 12.09% and 39.13% of the counties, respectively. The fourth and fifth zones, which have the lowest total value, are identified as low-carbon governance zones. The two zones cover 18.54% and 7.99% of the counties, respectively.
3.2. Map of Carbon Emission Governance Zones at the County Level
The classification results were visualized into a zoning map by GIS and the values of the 6 sectors in each zone were displayed with radar charts for an analysis of the differences, which together form the map of the county-level carbon emission governance zones (
Figure 4). The high-carbon problems and governance focuses of each zone were analyzed according to the total value and sector value of each zone.
The first zone is revealed as the high-carbon governance zone, in which the counties are located mainly in the Northeast Plain and the North Plain Area. Each sector value is high in the zone. The sector values of “Scale and structure (S)”, “Ecology (EC)”, and “Buildings (B)” are the highest of the 5 zones and the sectors value of “Economic development (ED)” and “Transportation (T)” are the second highest. The large-scale developments, ecological erosion, and housing energy consumption mainly caused the carbon-intensive model of development. Meanwhile, economic development and transportation play an important role. It could be inferred as these counties adopt the development path of expansion. They have expanded production through extensive land expansion to drive the growth of the secondary industry. This growth caused the erosion of carbon sink resources as a result of the lack of attention paid to ecological protection. In addition, extensive land expansion has led to the selection of bigger houses and the scattered distribution of buildings, resulting in high energy consumption caused by residential heating and long-distance transportation.
The second zone is a medium-carbon governance zone with low buildings value and high economy development value. It is widely distributed in the south and clustered in the southeast coastal area. The sector value of “B” of this zone is the lowest among the 5 zones, however, the sector value of “ED” is the highest. Meanwhile, the sector value of “S” is only second to the first zone. The value of “Energy efficiency (EE)” and “T” is also high. It can be concluded that the county housing energy consumption is low lie, due to two reasons: First, most of these areas do not have heating demands in winter. Second, the higher residential density in these region leads to higher residential energy efficiency. Meanwhile, the counties in this zone have a strong economic development orientation. It can be inferred that they take the mean of land expansion to promote the development of the secondary industry.
The third zone is revealed as medium-carbon governance zone of low scale and structure value and high energy efficiency value. The counties are mainly gathered in Inner Mongolia and the Loess Plateau Area. The sector value of “S” is the lowest in the 5 zones. However, the sector value of “EE” is the highest. The values of “B” and “T” are also high. Because of the limitation of plateau terrain, these counties tend to have less land expansion and less population concentration. Thus, the characteristics of scale and structure are not obvious, and the associated carbon emissions are less. But, at the same time, these areas have a high proportion of fossil energy applications, resulting in a large amount of energy consumption.
The fourth zone is revealed as a low-carbon governance zone with high transportation value. The counties belonging to the zone are mainly gathered in the western region and northeast border. They receive the lowest sector values of “S”, “ED”, “EC”, and “EE”. But, they also have a high sector value of “T”, which causes much carbon emissions. The low emissions of these counties can be inferred as backward economic development, lower modernization and urbanization level, and less population concentration. However, most of these counties have complex terrain and large land area, resulting in high energy consumption of long-distance transportation.
The fifth zone was revealed as the efficient low-carbon zone. The counties of the zone are scattered across the Northeast and Midwest. The sector values of “S”, “ED”, “EC”, “EE”, and “T” are all very low and the sector value of “T” “is the lowest of the 5 zones. The low-carbon development of such areas benefits from a compact land-use model, perfect public facilities, and soothing economic growth. This zone has a low potential for further emission reduction through governance.