1. Introduction
Carbon emissions in Chinese villages have significantly increased in the past decades. However, there is still a lack of methods for calculating these carbon emissions. This paper developed an operational approach for calculating carbon emissions from CO2, CH4 and N2O in villages, employing a bottom-up approach based on field investigations. This approach was subsequently applied to seven villages located in Northern China. In addition to quantifying carbon emissions, we conducted a thorough comparison and analysis on carbon emissions and sinks among the seven villages. The findings illuminate the diverse carbon emission scenarios across various village types, offering valuable insights to facilitate proper village planning and inspire well-informed recommendations for formulating effective strategies to promote low-carbon development in villages. To provide a comprehensive research context, a concise literature review is conducted as follows.
1.1. Carbon Emissions of Chinese Villages
Being one of the nations with the highest global carbon emissions, China has implemented a wide range of measures to mitigate its carbon footprint. Despite a gradual decline in the nation’s rural population due to the ongoing process of urbanization, as of 2022, approximately 491 million individuals still inhabit villages, accounting for roughly 35% of the total population [
1]. Characterized by their abundant natural ecological resources and rich carbon storage capacities, villages are consistently presumed to play a crucial role in environment preservation and ecosystems maintenance. However, the increase in economic activity and improvement in living standards in these regions have led to a rapid escalation in energy consumption and subsequent significant growth in carbon emissions. The carbon emissions in villages resulting from energy consumption for daily activities and production have undergone a substantial increase, escalating approximately sevenfold from 30 million tons in 1979 to 237 million tons in 2018 [
2]. Meanwhile, the energy consumption in agriculture continues to rise with the increasing crop production and widespread implementation of agricultural mechanization, witnessing a growth of 46% from 665 million tons in 1980 to 970 million tons in 2020 [
3]. Fortunately, rural areas boast abundant sustainable energy sources such as solar power, wind power and biomass resources that are crucial for reducing overall carbon emissions. Therefore, developing low-carbon villages can effectively contribute to the reduction of the nation’s overall carbon footprint.
1.2. Limited Researches on Carbon Emission and Sink Calculation of Chinese Villages
Currently, the predominant focus of research on Chinese villages lies in exploring the implementation of renewable energy [
4,
5,
6], while low-carbon development strategies are primarily directed towards sector-specific carbon emission calculations such as those pertaining to residential buildings [
7,
8] and agriculture [
9,
10,
11]. However, it is crucial to acquire a comprehensive understanding of carbon emission patterns at the village level through proper calculations, coupled with the identification of pertinent sectors that exert significant impact and offer potential for improvement. This knowledge is essential for devising tailored and efficient measures to mitigate emissions, thereby facilitating sustainable low-carbon strategies at the village scale.
Existing studies on carbon emission calculations in China primarily focus on national [
12,
13], provincial [
14] and city [
15,
16] scales, with limited research dedicated to the village scale. Unlike nations or cities, villages often lack comprehensive statistical data on carbon emission activities, posing challenges when applying calculation methods utilized at the national and city scales. Consequently, there remains a dearth of studies investigating the quantification of carbon emissions at the village level. For instance, Ref. [
17] developed a methodology for villages where tourism serves as the main industry. This research classified carbon emission sources into three categories: tourism-related, agricultural and community-based. The method was applied to two villages in Anhui province located in the Yangtze River Delta region of China. Similarly, Refs. [
18,
19] developed a method for calculating carbon emissions in rural areas and applied it to eight villages within the same region. However, this approach lacks comprehensive quantification of agricultural emissions, particularly those arising from livestock production which constitutes the predominant industry in most Chinese villages. Moreover, there is a need to update the emission factors utilized in these studies. Similar studies by have investigated villages in the same region [
20,
21,
22]; however, these methods were presented in a simplistic manner, confined to fuel consumption, electricity usage and transportation as sources of carbon emissions.
In conclusion, despite the existence of several studies focusing on the calculation of carbon emissions and sinks in Chinese villages, they have primarily concentrated on the Yangtze River Delta region, neglecting a comprehensive investigation into agricultural carbon emissions. Considering that villages in different regions possess distinctive characteristics, it is crucial to examine their carbon emissions and sinks separately to obtain a holistic understanding of the actual situations. Therefore, there is a need to develop a comprehensive approach applicable across various regions for assessing village-level carbon emissions. This approach has been applied to seven villages in Northern China as a case study.
1.3. Studies on Small Spatial Scale in Other Countries
Although limited studies were conducted at the village scale in China, several investigations on similar spatial scales have been carried out in other countries. For example, Ref. [
22] examined carbon emissions in the urban neighborhood of Barrio Tiro de Linea in Seville, Spain, proposing efficient decarbonization strategies. Comparable research was also undertaken in the Dubrovnik district of Gruž, Dubrovnik [
23], as well as in a typical European city neighborhood comprising 10,000 households and 23,000 residents [
24]. Furthermore, apart from urban areas, a similar methodology was applied to the campus of Delft University of Technology in the Netherlands to assist administrators in devising tailored and efficient decarbonization plans [
25]. These studies collectively demonstrate the effectiveness of assessing carbon emissions at small spatial scales and facilitating the implementation of customized decarbonization measures.
1.4. Regional Difference in Carbon Emissions of Chinese Villages
The carbon emissions of villages in China exhibit regional variations, particularly with regards to agriculture-related emissions which vary among different regions due to diversified agricultural production methods, predominant crops and livestock types. The study of [
26] calculated the agricultural carbon emissions based on data collected from 2009 to 2019, revealing distinct spatial differences among the eastern, western and central regions. Similar regional disparities were also observed in [
3], which analyzed relevant data spanning from 1980 to 2020. Furthermore, regional discrepancies resulting from energy consumption for daily life in villages significantly contribute to these variations. Research conducted by [
27] indicates that the geographical locations of Chinese villages greatly influence energy consumption patterns and subsequently lead to regional disparities in corresponding carbon emissions. This finding is further supported by the results from [
28], which examined life-related carbon emission data in Chinese villages from 2001 to 2013. Additionally, the transportation sector within China also exhibits evident regional disparities in terms of carbon emissions, as demonstrated in [
29]. Therefore, conducting region-specific calculations for carbon emissions is essential for obtaining a comprehensive understanding of village-level emission profiles.
2. Material and Method
This study presents a novel approach for quantifying and assessing carbon emissions and sinks at village level, incorporating both previous studies and field investigations. The proposed approach is applied to examine seven representative villages situated in Northern China as a case study.
2.1. Assessment Boundary of Carbon Emissions for Villages
The proposed approach primarily draws upon the guidelines outlined in the Guidelines for National Greenhouse Gas Inventory of the Intergovernmental Panel on Climate Change (IPCC) [
30] and the Guideline for Provincial Greenhouse Gas Inventory (GPGGI) [
31], both issued by China’s National Development and Reform Commission. While referring to both resources, the GPGGI serves as the principal guide for this study. Collaboratively compiled by nationally recognized research institutes and universities under the organization of the Climate Change Department of the National Development and Reform Commission of China in 2011, the GPGGI aims to enhance scientific rigor, standardization and feasibility of provincial greenhouse gas inventory compilation. Within the frameworks of the IPCC, tailored adaptions based on specific conditions observed in China have been implemented in the GPGGI.
The assessment boundary in this study is defined by the geographic limits of an administrative village. In China, an administrative village is an administrative division in rural areas, comprising one or more natural villages or a portion thereof, along with the surrounding collectively owned lands. The establishment of administrative villages aligns with the Organic Law of the Villagers’ Committee of the People’s Republic of China. In contrast, natural villages refer to settlements formed by families, households, clans or other social factors over an extended period in a naturally conductive environment.
2.2. Selection and Classification of Carbon Emission Sources and Sinks
The selection and classification of carbon sources are conducted based on a review of previous studies as well as field investigations into villages. As presented in
Table 1, the classification of carbon emissions and sinks is introduced as follows.
2.2.1. Sectors and Sub-Sectors
In this study, carbon sources and sinks are categorized into three components (life, production and ecology) in accordance with the national strategy of rural revitalization in China. The life component encompasses the building sector (carbon emissions from energy consumption, water usage and waste disposal) and the transportation sector. The production component includes the agriculture sector and the industry sector. The ecology component consists of the forestry sector and other land-use activity sectors serving as carbon sinks. Unlike [
18], this study integrates livestock into the agriculture sector to acknowledge its significance as a key agricultural industry.
High carbon-emission factories are excluded from the Industry Sector in this study for two reasons. Firstly, these factories typically belong to large corporations but are not owned by village residents, and they usually implement their own initiatives and strategies for carbon emission mitigation with separated measurements. Secondly, these factories contribute substantially to the overall emissions of villages, as evidenced in the study of [
18]. Therefore, incorporating their emissions would distort the accurate depiction of the emission scenarios in villages themselves. For example, the emissions from the cement production factory in Miaoqian, one of the villages under investigation, have been excluded from consideration in this study.
2.2.2. Categories
Under the sub-sector of residential buildings within the building sector presented in
Table 1, carbon emissions from tap water usage are included as a category, which differs from the regulations outlined in GPGGI [
31]. Regarding solid waste disposal, the previous study of [
18] only considered the carbon emissions resulting from incineration. However, landfilling accounts for 73% of solid waste disposal in villages of Northern China, as stated in [
32]. Therefore, this study includes landfill as a category in the sub-sector of solid waste.
In the agriculture sector, agricultural machinery is classified as a sub-sector, while the use of agricultural film and ploughing are included as separate categories. These classifications differ from the regulations set by GPGGI [
31]. Unlike [
18], which only considered direct emissions from fertilizer application, this study also incorporates the indirect emissions arising from settlement and leaching due to fertilizer application. The study of [
18] classified straw returning as carbon sinks, while [
33] shows that carbon emissions exceed carbon sinks associated with straw returning. Therefore, this study categorizes straw returning as carbon emissions within the sub-sector of crops. Moreover, unlike [
18], which applied a uniform carbon emission factor to assess pesticide usage across different crops, this study separately calculates carbon emissions for each crop type, considering the specific pesticides used. Additionally, in line with the findings of [
34], our study also incorporates CH
4 and N
2O from agriculture alongside CO
2 emissions.
Regarding the calculation of carbon sinks, Ref. [
18] broadly classified land use into woodland and grassland. However, forests are further categorized as “arbor forests” and “bamboo groves, economic and shrub forests” in this study, based on distinct methods for calculating carbon sinks as referenced in [
31]. In arbor forests, carbon sinks primarily result from the annual growth of trees, whereas in bamboo groves, economic forests and shrub forests, changes in forest area predominantly contribute to carbon sinks dynamics, occasionally resulting in negative values due to the reduction in forest areas.
2.3. Categorizing Emissions by Scope
According to the categorization methodology proposed by [
35], this study encompasses three scopes: Scope 1 refers to carbon emissions from sources within the village boundary, which account for the majority of emissions; Scope 2 includes carbon emissions resulting from grid-supplied electricity and heating within the village boundary, encompassing electricity usage for residential buildings, factories, product manufacturing, irrigation and natural gas usage for heating purposes; Scope 3 comprises all other carbon emissions occurring outside the village boundary due to activities taking place within it, including out-of-boundary solid waste disposal, sewage treatment and transportation.
2.4. Calculation Method for Carbon Emissions and Sinks
2.4.1. Calculation Method for Carbon Emissions
The calculation method utilized in this study follows a specific sequence. Firstly, it utilizes the methodology outlined in the GPGGI. Secondly, it applies the carbon emission factor method as per IPCC guidelines, which involves multiplying the activity level of carbon emission sources by their corresponding carbon emission factors. In cases where specific carbon emissions are not covered by GPGGI (such as those arising from tap water, agricultural machinery, agriculture films and ploughing), the carbon emission factor method is then applied.
In addition to CO
2, this study also includes the emissions of two other primary greenhouse gases: CH
4 and N
2O. The N
2O emissions primarily result from fertilizer use, straw returning, fecal management and fuelwood usage, while CH
4 emissions mainly arise from intestinal fermentation, fecal management, fuelwood usage and sewage treatment. Since this study focuses on quantifying total greenhouse gas emissions without distinguishing among the three types individually, they are collectively referred to as carbon emissions in this paper. These emissions are measured in a standardized unit known as carbon dioxide equivalents (t CO
2-e), which represents the quantity of each of the three greenhouse gases multiplied by their respective 100-year Global Warming Potential (GWP
100): CO
2 GWP
100 = 1, CH
4 GWP
100 = 29.8, N
2O GWP
100 = 273 [
36].
2.4.2. Calculation Method for Carbon Sinks
Considering that ecosystems emit CO
2 through respiration, the calculation of carbon sinks involves reducing carbon emissions from forestry and other land use activities. This study takes into account the carbon emissions from arbor forest. As stipulated in GPGGI [
31], the carbon sinks of arbor forest are calculated using the following formula:
Carbon stock change (t) is calculated with the following formula:
Consumed carbon stock (t) is calculated with the following formula:
: total storage volume (m3); : annual growth rate of storage volume (%); : annual consumption rate of storage volume (%); : basic wood density (t/m3); : biomass conversion coefficient.
Regarding the carbon sinks of bamboo groves, economic and shrub forests, the following formula is used, as stipulated in GPGGI [
31]:
: annual area change (hm2); : average biomass per area (t/hm2).
2.5. Collection Methods for Carbon Activity Levels
The data collection methods employed in this study for determining carbon activity levels are presented below.
Table 2 provides the corresponding methods for each emission source.
- (1)
Field investigation: Initial field investigations are conducted to gain a comprehensive understanding of the target villages, including assessments of land types, industry classification, and the socioeconomic dynamics of local residents.
- (2)
Questionnaires: Conducting surveys among local residents serves as a crucial approach for collecting data on diverse emission sources. Interviews with the village committees facilitate the gathering of information on population, industry and land use. Questionnaires administered to local villagers are indispensable in obtaining data regarding daily energy consumption, water usage, and other material applications for buildings and agriculture. Moreover, questionnaires specifically targeted at large-scale livestock breeders collect data associated with livestock-related emissions.
- (3)
Statistical data: Statistical data is utilized for emissions that are not directly obtainable from questionnaires, including the volume of water usage for irrigation, the usage level of agricultural films, the amount of domestic sewage and the weight of solid waste (
Table 2).
- (4)
Big-data platform: With the advancement of information technology and statistical methods, China has developed various intelligent management platforms based on big-data technologies. In this study, the NFSSMP [
37] is utilized to access land use information in detail for villages. The NFSSMP, a platform developed by the National Forestry and Grassland Administration and National Park Administration, offers a GIS-based national forest resources archive information database. This study employs the NFSSMP to obtain the geographic boundaries of villages, forest areas, tree species, diameter at breast height, stock volume per hectare (in 0.1 m
3/ha) and forest stock volume (in 0.1 m
3), which are utilized to calculate carbon sinks.
Table 2.
Activity level data and collection sources.
Table 2.
Activity level data and collection sources.
Activity | Unit | Activity Level Data and Collection Sources |
---|
Energy |
Electricity | kWh | Questionnaire with villagers |
Natural gas | m3 | Questionnaire with villagers |
LPG | kg | Questionnaire with villagers |
Coal | kg | Questionnaire with villagers |
Firewood | kg | Questionnaire with villagers |
Gasoline | kg | Questionnaire with villagers |
Water |
Volume of tap water | L/person | 100 L/d·person, statistical data [38] |
Waste |
Weight of solid waste | kg/person | 0.775 kg/d·person, statistical data [39] |
Weight of sewage | L/person | 100 L/d·person, statistical data [38] |
Industrial production |
Plastic product output values | CNY | Interview with the factory owner |
Chemical product output values | CNY | Interview with the factory owner |
Agricultural machinery |
Diesel consumption of farm machinery | Kg | Questionnaire with villagers |
Electricity usage for irrigation | kWh/mu * | Calculated with the following formula: Electricity use for irrigation:
, water use amount per area in Henan: Maize: 91 m3/mu [40]; Wheat: 161 m3/mu [41]; , water utilization coefficient: 0.8; , electricity conversion coefficient for irrigation: 3.196 m3/kWh [42]. |
Livestock |
Intestinal fermentation—livestock number | - | Questionnaire with owners |
Fecal management—livestock number | - | Questionnaire with owners |
Crops |
Ploughing—cultivated land area | km2 | Interview with village committee |
Pesticide—crop cultivation | kg | Questionnaire with villagers |
Agricultural film—cultivation land area | mu | Henan: 1.35 kg/mu, Shandong: 2.76 kg/mu, statistical data [43] |
Fertilizer | kg | Questionnaire with villagers |
Straw return | kg | Questionnaire with villagers |
2.6. Collection of Carbon Emission Factors
To ensure enhanced accuracy in the calculation results, this study employs a hierarchical approach for the selection of carbon emission factors. Initially, factors extracted from the latest scientific publications are utilized, followed by regional factors provided by national authority departments. Finally, factors recommended by the GPGGI and IPCC are employed. The detailed carbon emission factors and their references are illustrated in
Table 3. Among all the emission factors considered, three specific ones are calculated.
2.6.1. Emission Factor for Energy Sources
The following formula, being adjusted based on GPGGI [
31], is utilized to calculate the carbon emission factor of energy sources:
The average heating value of each energy source is cited from [
31], while the carbon amount per unit of heating value and carbon oxidation rate are sourced from the updated values of 2020 released in [
45].
2.6.2. Emission Factor for Electric Irrigation
The carbon emission factor for electric irrigation commonly used in current studies is derived from the study of [
53], which focuses on data from USA and India. However, it fails to account for the specific conditions in China. In China, the Ministry of Water Resources regulates the water volume for irrigation through a quota system.
This study utilizes regional irrigation water quotas delineated in [
40,
41] to determine the allocated water volume for individual villages. Subsequently, electricity consumption for irrigation is computed by correlating the allocated water volume with the corresponding electricity conversion coefficient (i.e., the water volume supplied per kilowatt-hour of electricity). Finally, based on this computed electricity consumption, a more precise estimation of the carbon emission factor for electric irrigation is determined to provide an accurate depiction of circumstances in China.
2.6.3. Emission Factor for Pesticides
The emission factor for pesticides utilized in recent studies in China is derived from experimental values obtained in the USA [
54], which may not accurately reflect the specific circumstances in China. Therefore, our study adopts values from a previous investigation [
51], which were calculated based on national questionnaires conducted in Chinese villages in 2012.
2.7. Carbon Emission Calculation for Seven Villages in Northern China
The carbon emission calculation methodology is implemented and applied to villages situated in Northern China, which is a prominent agricultural area. Three villages from Shandong and four villages from Henan provinces were selected, with their geographical locations depicted in
Figure 1. Below, we provide essential details and collection methods for assessing carbon emissions.
2.7.1. Selection of Investigated Villages in This Study
The selection of the seven villages was primarily considered based on the authors’ accessibility for conducting field investigations. However, other factors were also considered. Based on the categorization of life-related carbon emissions [
28], these villages fall within the high-to-middle range compared to other regions in China. Additionally, according to agricultural emission data analyzed in [
3], the examined area in this paper ranks at a high level due to its elevated economic status, greater reliance on commercial energy resources and increased presence of agricultural residuals. These villages predominantly cultivate maize and wheat as their main crops. Apart from their common characteristics, the seven villages host a diverse range of industries which enable us to examine the impact of different industrial sectors on village-level emissions.
2.7.2. Information of the Investigated Villages
The seven villages exhibit variations in their primary industries, terrains, population and land areas. Their industries encompass grain production, livestock breeding, tourism, fruit cultivation, fishery and plastic/chemical materials production. The terrains span from plains to mountains and coastal regions.
Table 4 presents the essential information for the seven villages, including population, number of households, land area, average income, terrain type and primary industry.
2.7.3. Data Collection for Carbon Activity Levels
An investigation was conducted in August 2023 across seven villages to collect data for carbon emission calculations. The methods outlined in
Section 2.5 were employed to determine activity levels of carbon emission sources, and
Table 2 presents the specific method used for each source within the seven villages. Interviews were conducted with village committees, livestock breeders and factory owners, along with 30 questionnaires administered to locals in each village. Despite the limited sample size of the survey, it is worth noting that Chinese villages typically operate small-scale agricultural economies where individual households are granted land-use rights [
55], fostering a relatively egalitarian society where the households within the same village share similar lifestyles.
2.7.4. Uncertainty Analysis
The primary source of uncertainty lies in the carbon activity levels and carbon emission factors. Direct collection of local data on specific carbon activities was not feasible, such as the amount of solid waste and sewage, consumption of agriculture films and volume of water used for irrigation; therefore, statistical data were utilized instead. This introduces a certain degree of uncertainty when quantifying carbon emissions. Although this study has incorporated the most up-to-date carbon emission factors available, some factors were obtained from a decade ago, including the ploughing factor updated in 2007, industrial products factor updated in 2012 and solid waste factor updated in 2011. To reduce the uncertainty associated with calculating carbon emissions in the study, it is recommended to introduce more recent and localized carbon emission factors.
3. Results
The total carbon emissions and sinks are recorded in
Table 5, while the per capita carbon emissions for the seven villages have been computed and presented in
Appendix A.
3.1. Result of Carbon Sinks
Comparing the per capita carbon sinks of each village (
Table 5) with the national average per capita carbon sinks in 2020 (0.6 t CO
2e/person) [
56], most villages exhibit lower levels than the national average, with the exception of Yidoushui. Among the seven villages, Yidoushui stands out with highest per capita carbon sinks at 14.71 t CO
2e/person, followed by Jiangjia and Zaiwan at 0.55 t CO
2e/person and 0.22 t CO
2e/person, respectively. The per capita carbon sinks of villages are strongly correlated with the per capita forest area, which is particularly abundant in these three villages, as indicated in
Table 4. Yidoushui’s carbon sinks significantly outweigh its carbon emissions due to its extensive mountainous forest coverage, while conversely, the carbon emissions in other villages exceed their carbon sinks by a significant margin. Notably, Miaoqian exhibits negative value of carbon sinks, attributed to a decline in forest area compared to the previous year.
3.2. Result of Carbon Emissions
This study compares the per capita carbon emissions of the seven villages (
Table 5) with their corresponding provincial average data. According to the data provided by [
57], the average per capita carbon emissions in Shandong province was 10.27 t CO
2e/person in 2021, while it was 5.11 t CO
2e/person in Henan province. It is evident that the per capita carbon emissions in most villages are lower than the provincial averages, with the exception of Qiganshi and Shangliuzhuang.
Figure 2 illustrates the per capita carbon emissions of the seven villages. It is worth noting that Qiganshi exhibits high carbon emissions attributed to agricultural machinery, particularly diesel consumption in fishing boats. Similarly, Shangliuzhuang’s carbon emissions from its industrial activities, particularly in plastic and chemical materials production, surpass other carbon sources. Hence, the high carbon emissions in Qiganshi and Shangliuzhuang mostly result from emissions related to their primary industries, namely fishery and industry respectively. Therefore, further analysis is needed to understand the relationship between per capita carbon emissions and the primary industry of each village.
Among the seven villages, Miaoqian solely relies on crop cultivation as its primary industry. In comparison to Miaoqiao, the remaining six villages are categorized into three levels based on their per capita carbon emissions, as follows:
- (1)
Qiganshi and Shangliuzhuang exhibit per capita carbon emissions ranging from 5 to 9 t/person, with fishery and industrial production as their primary industries. These industries are recognized for their high carbon emissions compared to other sources.
- (2)
Zaiwan and Zhangjiazhuang exhibit per capita carbon emissions of approximately 3 t/person, with tourism and livestock breeding as their primary industries.
- (3)
Yidoushui and Jiangjia exhibit per capita carbon emissions below 2 t/person, with tourism and fruit cultivation as their primary industries. The two industries demonstrate relatively low levels of carbon emissions.
Although tourism is the primary industry for both Zaiwan and Yidoushui, their per capita carbon emissions differ significantly. Yidoushui, located in a mountainous area, attracts tourists who prioritize natural sightseeing experiences with shorter stays and simple accommodations, thus resulting in lower carbon emissions compared to Zaiwan. Therefore, the per capita carbon emissions in these villages are highly influenced by their primary industries. Villages in Northern China tend to harbor high-carbon emission industries like fishery, industrial production and livestock breeding.
3.3. Comparison with the Results of Other Studies
The results of this study are compared with those from other studies, with a particular emphasis on the emissions from different rural regions of China as well as emissions from comparable areas at similar spatial scales in various countries.
3.3.1. Comparison with the Results in Other Chinese Villages
Among the several studies cited in
Section 1.2, the study of [
18] presented a detailed calculation method and findings on carbon emissions from eight villages located in Yangtze River Delta region. Consequently, a comparative analysis is conducted between the results of [
18] and those of this study to identify disparities in regions.
Carbon emissions: Despite the carbon emissions from livestock being excluded in [
18], the average per capita carbon emissions at 3.13 t CO
2e/person in our study already possesses significantly lower value than that of the Yangtze River Delta region at 6.45 t CO
2e/person. This discrepancy can be attributed to two factors. Firstly, the per capita income of the villages in this study (18,066 CNY/yea·person) is considerably lower than that of the Yangtze River Delta region (13,813 CNY/year·person). Secondly, the industries prevalent in our study differ from those examined in [
18], which exhibits relatively higher levels of carbon emissions.
Carbon sinks: Yidoushui is excluded from this comparison due to its significantly extensive mountainous forest coverage, which distinguishes it from the other six villages. The average carbon sinks of the remaining villages (0.13 t CO2e/person) in the study are considerably lower than that of the Yangtze River Delta region (0.47 t CO2e/person). Despite a lower average per capita land area in the Yangtze region (0.003 km2/person) compared to the northern regions (0.006 km2/person), the forest coverage in the villages within the Yangtze region surpasses that of their counterparts in the latter.
Conclusion: The carbon emissions of the investigated villages in the northern regions are lower than those in the Yangtze River Delta region, primarily attributed to their relatively lower per capita income. The carbon sinks of the investigated villages in the northern regions generally exhibit a lesser magnitude compared to those in the Yangtze River Delta region due to their relatively lower forest coverage.
3.3.2. Comparison with Carbon Emissions of Europe at Similar Spatial Scale
The study of [
58] estimated the carbon emissions for the Belgian town of Roeselare without considering carbon sinks, resulting in per capita carbon emissions of 2.88 t CO
2e/person. Similarly, Ref. [
24] calculated the carbon emissions of a typical European city neighborhood, yielding per capita carbon emissions of 3.01 t CO
2e/person. These two cases share a comparable spatial scale with Chinese villages. Among the seven villages investigated in this paper, only Shangliuzhuang and Qiganshi exhibit higher per capita carbon emissions compared to Roeselare and the European neighborhood due to significant contributions from industries such as plastic and chemical production in Shangliuzhuang and fishery in Qiganshi. Conversely, the remaining five villages demonstrate lower per capita carbon emissions than those of the two referenced cases.
3.4. Comparison among Different Carbon Emission Sectors
The comparison in carbon emission sectors and subsectors across the seven villages is outlined in
Table 6. Among the carbon emission sectors, the sequence of emission sources from high to low is as follows: diesel consumption in fishing boats, manufacturing of plastic/chemical products, energy consumption in buildings, agriculture, transportation and waste disposal.
3.4.1. Diesel Consumption in Fishing Boats and Manufacturing
In Shangliuzhuang, diesel consumption in fishing boats and plastic/chemical production are the primary contributors of carbon emissions, accounting for approximately 61% and 78% of total carbon emissions, respectively. Therefore, developing low-carbon villages requires a transformation in these two industries. However, such industries are uncommon in the investigated villages. After excluding emissions from the two industries, the relative importance of other emission sources is presented as follows.
3.4.2. Energy and Water Usage in Buildings
As demonstrated in
Table 6, the carbon emissions from energy and water usage in buildings account for approximately 30–70% of the total carbon emissions in most villages, except for Shangliuzhuang and Qiganshi. Among all energy sources, electricity and coal usage exhibit the highest emission level as they serve as the primary energy sources. Variations in energy usage emissions among villages stem from different energy source structures and lifestyles. For instance, the carbon emissions from electricity usage in Zaiwan significantly exceed those in other villages due to its reliance on electricity for heat pumps that provide a high level of comfort for tourists, resulting in increased electricity consumption. In contrast, Yidoushui exhibits considerably higher carbon emissions from coal usage than from electricity due to households relying on coal for heating while possessing fewer electrical appliances compared to other villages.
3.4.3. Agriculture
As depicted in
Table 6, agriculture accounts for approximately 20% of total carbon emissions in most villages. These emissions primarily originate from livestock breeding and crop cultivation, with minimal contribution from agricultural machinery. As shown in
Appendix A, the carbon emissions from livestock breeding far surpass those from crop cultivation in Shangliuzhuang and Zhangjiazhuang, where a substantial number of livestock are raised. Shangliuzhuang hosts 6000 pigs, 200 cattle and 200 sheep, while Zhangjiazhuang raises 2300 pigs. As presented in
Appendix A, emissions from straw returning constitute half of the total carbon emissions for crops, being the most significant source in crop cultivation.
3.4.4. Transportation
The proportions of carbon emissions from transportation in most villages range from 5% to 25% of the total carbon emissions, as demonstrated in
Table 6. Despite this wide range, the per capita carbon emissions are similar across all of the villages (
Table 6). This portion of carbon emissions primarily arises from the gasoline consumption for private cars. Jiangjia stands out with the lowest per capita carbon emission due to its prevalent use of motorcycles for transportation rather than the reliance on private cars, as observed in other villages.
3.4.5. Waste Disposal
According to
Table 6, roughly 10% of carbon emissions originate from waste disposal, encompassing solid waste and domestic sewage treatment. Notably, the emissions from sewage treatment are lower in comparison to solid waste disposal, with only Yidoushui and Zaiwan currently possessing sewage treatment facilities among all the investigated villages. In most villages, around 70% of solid waste is managed through landfilling, resulting in a significant level of carbon emissions. However, Yidoushui lacks centralized solid waste processing facilities and resorts to open-air disposal. Although this approach yields zero carbon emissions, it raises concerns regarding local environmental pollution.
4. Discussion
4.1. Findings of Study
The carbon emissions and sinks of the seven villages are compared in this study. Concerning carbon sinks, the per capita carbon sinks in most villages fall below the national average, with the exception of Yidoushui. The per capita carbon sinks in villages are highly relevant with their respective forest areas. Surprisingly, the carbon emissions of most villages exceed their sink levels. Hence, it is crucial to maintain stable forest growth and implement strict deforestation control to foster the development of low-carbon villages.
With the exception of two villages hosting industries with exceptionally high carbon emissions, the per capita carbon emissions in the remaining villages are below the average level of their respective provinces. Notably, primary industries significantly impact carbon emissions. When comparing the villages where crop cultivation serves as the primary industry with others, per capita carbon emissions can be categorized into three levels from highest to lowest. The first level includes villages engaged in fishery and industrial production, the second level comprises villages involved in livestock breeding and catering to high-comfort-need and longer-stay tourism, and the third level encompasses villages which focus on fruit cultivation and catering to low-comfort-need and shorter-stay tourism. These findings highlight the importance of implementing low-carbon industries for developing sustainable rural communities.
Furthermore, a comparative and analytical assessment is conducted on different sectors of carbon emission sources. Excluding the significantly high carbon emissions from fishery and industrial production, the primary sectors that exert influence on carbon emissions in villages, ranked in descending order, include energy consumption in buildings, agriculture, transportation and waste disposal:
- (1)
Carbon emissions from energy consumption in buildings account for approximately 30–70% of the total emissions, which are influenced by local energy structures and lifestyles.
- (2)
Agriculture accounts for around 20% of total carbon emissions, primarily coming from livestock breeding and crop cultivation.
- (3)
Transportation-related emissions range from 5% to 25%, mainly attributed to private car usage.
- (4)
Waste disposal contributes roughly 10% to the overall emissions.
4.2. Implications
4.2.1. Theoretical Implications
Carbon emission calculation method for small spatial scale: This study presents a comprehensive methodology for quantifying carbon emissions in Chinese villages, employing a localized approach with detailed methods to collect activity levels using updated emission factors. Moreover, this approach can be extrapolated to other spaces with similar spatial scale such as townships and districts. The findings of this study address the existing research gap in quantifying carbon emission at this specific scale in China.
4.2.2. Practical Implications
Necessity for developing low-carbon villages: Among the seven villages, only one village demonstrates its carbon sinks exceeding carbon emissions, while the majority of villages exhibit significant carbon emissions in comparison to their respective carbon sinks. Notably, certain villages display even higher levels of carbon emissions than those observed in the Yangtze River Delta region and the European town along with its neighborhood. These findings underscore the urgent need for policy makers to prioritize addressing carbon emissions in rural areas and emphasize the imperative of developing low-carbon villages.
Formulating quantitative strategies for low-carbon development based on findings: The comparison of carbon emissions across different sectors in villages facilitate a comprehensive understanding of the structural dynamics of carbon emissions. By utilizing calculated carbon emissions and sectoral composition, policymakers can formulate precise low-carbon development strategies that allocate efforts and investment proportionally to each sector’s contribution to carbon emissions. Moreover, adopting a quantitative approach to plan low-carbon village development enhances policy coherence and consistency.
Developing low carbon industries: The comparison of the results obtained from this study with those derived from other regions both within China and abroad, as well as the intra-village comparison, collectively demonstrates a significant influence of industries located within villages on total carbon emissions. Therefore, the development of low-carbon villages necessitates the presence of low-carbon industries.
4.3. Limitations
Limited number of investigated villages: Only seven villages have been included in this study due to the constraints of the research period and limited accessibility for field investigation. However, a greater number of investigated villages would enhance the comprehensiveness in representing the selected region.
Statistical data of the region: Owing to the lack of adequate facilities for measuring and recording carbon emission data in these villages, the collection of certain carbon emission activity levels has become a challenge. To bridge this data gap, this study utilized regional statistical data, encompassing solid waste and sewage, agriculture film usage and water consumption for irrigation purposes. However, the statistical data of the region may not accurately reflect the actual situations of local villages.
4.4. Future Study
An increased number of villages in this region should be investigated and assessed. Based on the findings on carbon emissions, a correlation analysis should be performed between village-level carbon emissions and influential factors such as industry types, income levels, terrains, and energy structures. To facilitate an in-depth analysis of their impact on carbon emissions in villages, expanding the investigation scope to encompass a wider range of industries is advised. Furthermore, to acquire more localized and accurate data, it is recommended to deploy equipment within the villages for the purpose of measuring and recording carbon emission data.
5. Conclusions
This study presents an operational approach for calculating carbon emissions from CO2, CH4 and N2O in villages of Northern China based on field investigations. The carbon emission sources in villages are classified into buildings, transportation, industry, agriculture, forestry and other land uses. Seven villages were investigated in this region to estimate their carbon emission activity levels using data collected from field surveys, questionnaires, statistical records and big-data platforms. Their carbon emissions were calculated across the seven villages. The proposed methodology specifically focuses on small spatial scales in rural areas and bridges the gap in research on carbon emission calculation at this scale in Chinese villages. It helps guide low-carbon planning and design at village levels while facilitating the implementation of low-carbon development strategies and policies. These findings offer valuable insights into actual situations regarding carbon emissions in rural areas of Northern China, providing a basis for future in-depth investigations and studies on rural low-carbon development.
6. Recommendations
Based on the carbon emission calculation of the seven villages and their comparative analysis, the following recommendations for low-carbon development of villages in Northern China are formulated:
- (1)
Carbon Sinks: Forests play a crucial role as the primary source of carbon sinks in villages. Policymakers should enforce stringent measures to prevent deforestation while promoting the expansion of forest areas.
- (2)
Promoting Low-Carbon Industries: The carbon emissions of villages are significantly influenced by their primary industries. To promote the development of low-carbon villages, it is imperative to foster low-carbon industries while simultaneously implementing initiatives aimed at augmenting villagers’ income.
- (3)
Reducing Building Energy Consumption and Utilizing Renewable Energy: Carbon emissions from the building energy sector exhibit the highest level in most villages. Developing low-carbon villages should prioritize reducing the fossil energy consumption in buildings, particularly coal and fuelwood, while also promoting the adoption of renewable energy sources. However, the utilization of renewable energy remains limited among the surveyed villages, with less than one-third of buildings utilizing solar heaters or PV systems. Therefore, it is imperative to establish an effective system for harnessing renewable energy based on rural building characteristics.
- (4)
Developing Low-Carbon Livestock Farming and Enhancing Manure Management: Livestock breeding is a key industry in Northern China, characterized by relatively high carbon emissions. To mitigate this aspect of emissions, adjusting the nutritional composition of livestock feed can effectively reduce methane emissions from ruminant animals’ digestive systems. Additionally, harnessing manure for household energy and organic fertilizer in agriculture presents an indispensable solution.
- (5)
Developing Low-Carbon Crop Cultivation: The reduction of carbon emissions from crop cultivation can be achieved through various strategies. It is imperative to control the burial depth and utilize straws for heating or electricity generation in order to mitigate carbon emissions from straw returning. Other decarbonization approaches encompass the development of efficient techniques for nitrogen fertilizer application, promotion of agricultural film recycling, the adoption of biodegradable agricultural films and the reduction of pesticide usage.
- (6)
Encouraging Low-Carbon Transportation: The prevalence of private car usage in rural areas can be attributed to the inadequate of public transportation. To foster low-carbon development, it is imperative to bolster public transportation infrastructure for public transportation. Additionally, there should be a concerted effort to encourage the adoption of electrical vehicles and establish an extensive network of charging stations.
- (7)
Improving Waste Disposal Techniques and Enhancing Sewage Treatment: Conventional methods of solid waste disposal result in significant carbon emissions. It is recommended to advance low-carbon waste management techniques such as harnessing residual heat generated during waste processing for electricity generation. The carbon emissions from sewage treatments are not substantial, while only two villages have sewage treatment facilities. This deficiency significantly impacts the local residential environment, underscoring the necessity to enhance sewage treatment in villages.
Author Contributions
T.D.: Conceptualization, methodology, formal analysis, validation, writing. Y.J. and Y.Z.: supervision, reviewing and editing. Z.J.: methodology, formal analysis. J.W.: investigation, data curation, writing. J.Z.: investigation, data curation, writing. Z.C.: reviewing and editing. All authors have read and agreed to the published version of the manuscript.
Funding
This study received the support from the following funding sources: National Key R&D Program of China (2022YFC3803801 in 2022YFC3803800); China Postdoctoral Science Foundation project: Quantitative Carbon-Neutrality-Roadmap Design for Villages and Performance-based Optimization Design Method (2022M713015); Technology Innovation Fund project of China Architecture Design and Research Group: Case Study on Roadmap for Carbon Neutrality in Chinese villages and Performance Optimization Design (1100C080220331). The authors express their sincere gratitude for the grants received. Special thanks to Jin Fu from the National Engineering Research Center for Human Settlements of China for English revision and proof reading.
Data Availability Statement
Conflicts of Interest
All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.
Appendix A
Table A1.
Calculated per capita carbon emissions and sinks of investigated villages.
Table A1.
Calculated per capita carbon emissions and sinks of investigated villages.
| | | | Per Capita Carbon Emissions and Sinks (kg CO2e/Person) |
---|
Miaoqian | Yidoushui | Shangliuzhuang | Zaiwan | Zhangjiazhuang | Jiangjia | Qiganshi |
---|
Carbon emissions | Buildings | Residential buildings | Electricity | 487 | 140 | 408 | 1742 | 775 | 852 | 521 |
Natural gas | 0 | 0 | 0 | 93 | 0 | 0 | 0 |
LPG | 43 | 54 | 44 | 45 | 85 | 115 | 70 |
Coal | 0 | 567 | 0 | 0 | 573 | 174 | 540 |
Fuelwood-CH4 | 0 | 56 | 0 | 0 | 2 | 10 | 0 |
Fuelwood-N2O | 0 | 20 | 0 | 0 | 1 | 4 | 0 |
Tap water | 8 | 8 | 8 | 8 | 8 | 8 | 8 |
Commercial and public buildings | Electricity | 1 | 2 | 10 | 17 | 19 | 1 | 9 |
Solid waste | Landfill | 196 | 0 | 196 | 196 | 228 | 228 | 228 |
Incineration | 33 | 0 | 33 | 33 | 39 | 39 | 39 |
Sewage | sewage treatment | 0 | 1 | 0 | 1 | 0 | 0 | 0 |
Transportation | Road transportation | Road transport | 301 | 311 | 296 | 233 | 238 | 99 | 538 |
Industry | Industrial production | Plastic products industry | 0 | 0 | 1906 | 0 | 0 | 0 | 0 |
Chemical products industry | 0 | 0 | 1619 | 0 | 0 | 0 | 0 |
Agriculture | Agricultural machinery | Agricultural machinery—Diesel | 3 | 2 | 3 | 2 | 6 | 15 | 7177 |
Electric irrigation—Maize | 0 | 0 | 10 | 0 | 0 | 0 | 0 |
Electric irrigation—Wheat | 0 | 0 | 18 | 0 | 0 | 0 | 0 |
Livestock | Intestinal fermentation | 195 | 0 | 400 | 353 | 161 | 222 | 2 |
Fecal management | 85 | 3 | 733 | 70 | 564 | 61 | 17 |
Crops | fertilizer use-direct emission | 7 | 16 | 8 | 3 | 21 | 26 | 3 |
fertilizer use—settlement | 1 | 3 | 1 | 1 | 4 | 5 | 1 |
fertilizer use—leaching | 2 | 4 | 2 | 1 | 5 | 7 | 1 |
return straw—Maize | 15 | 33 | 31 | 7 | 29 | 0 | 0 |
return straw—Wheat | 1 | 5 | 3 | 1 | 3 | 0 | 0 |
Pesticide use—Maize | 2 | 4 | 4 | 1 | 9 | 11 | 1 |
Pesticide use—Wheat | 1 | 5 | 3 | 1 | 6 | 8 | 1 |
Agricultural film use | 10 | 21 | 10 | 4 | 12 | 15 | 2 |
Ploughing | 1 | 1 | 1 | 0 | 1 | 2 | 0 |
Carbon sinks | Forest and land use | Forests | Arbor forests | 74 | 14,394 | 26 | 143 | 31 | 554 | 3 |
Bamboo groves, economic and shrub forests | −121 | 312 | 0 | 77 | 0 | 0 | 0 |
Total carbon emissions | 1393 | 1257 | 5746 | 2811 | 2789 | 1902 | 9158 |
Total carbon sinks | −47 | 14,706 | 26 | 220 | 31 | 554 | 3 |
Net carbon emissions 1 | 1440 | −13,449 | 5720 | 2590 | 2758 | 1348 | 9154 |
References
- Regional Urban and Rural Related Data in 2022. Available online: https://www.ndrc.gov.cn/fggz/fgzy/jjsjgl/202301/t20230131_1348084_ext.html (accessed on 4 April 2024).
- Rural Energy Transformation Is Imminent. Available online: http://www.moa.gov.cn/ztzl/ymksn/jjrbbd/202201/t20220120_6387263.htm (accessed on 4 April 2024).
- Fan, Z.; Song, C.; Qi, X.; Zeng, L.; Wu, F. Accounting of Greenhouse Gas Emissions in the Chinese Agricultural System from 1980 to 2020. Acta Ecol. Sin. 2022, 12, 9471–9481. [Google Scholar]
- Li, J.; Chen, C.; Walzem, A.; Nelson, H.; Shuai, C. National Goals or Sense of Community? Exploring the Social-Psychological Influence of Household Solar Energy Adoption in Rural China. Energy Res. Soc. Sci. 2022, 89, 102669. [Google Scholar] [CrossRef]
- Zeng, X.; Zhao, Y.; Cheng, Z. Development and Research of Rural Renewable Energy Management and Ecological Management Information System under the Background of Beautiful Rural Revitalization Strategy. Sustain. Comupting Inform. Syst. 2021, 30, 100553. [Google Scholar] [CrossRef]
- Li, C.; Zhou, D.; Zhang, L.; Shan, Y. Exploration on the Feasibility of Hybrid Renewable Energy Generation in Resource-Based Areas of China: Case Study of a Regeneration City. Energy Strateg. Rev. 2022, 42, 100869. [Google Scholar] [CrossRef]
- Ma, M.; Ma, X.; Cai, W.; Cai, W. Low Carbon Roadmap of Residential Building Sector in China: Historical Mitigation and Prospective Peak. Appl. Energy 2020, 273, 115247. [Google Scholar] [CrossRef]
- Du, Q.; Han, X.; Li, Y.; Li, Z.; Xia, B.; Guo, X. The Energy Rebound Effect of Residential Buildings: Evidence from Urban and Rural Areas in China. Energy Policy 2021, 153, 112235. [Google Scholar] [CrossRef]
- Luo, J.; Huang, M.; Bai, Y. Promoting Green Development of Agriculture Based on Low-Carbon Policies and Green Preferences: An Evolutionary Game Analysis. Environ. Dev. Sustain. 2024, 26, 6443–6470. [Google Scholar] [CrossRef]
- Yang, H.; Wang, X.; Bin, P. Agriculture Carbon-Emission Reduction and Changing Factors behind Agricultural Eco-Efficiency Growth in China. J. Clean. Prod. 2022, 334, 130193. [Google Scholar] [CrossRef]
- Xu, G.; Li, J.; Schwarz, P.M.; Yang, H.; Chang, H. Rural Financial Development and Achieving an Agricultural Carbon Emissions Peak: An Empirical Analysis of Henan Province, China. Environ. Dev. Sustain. 2022, 24, 12936–12962. [Google Scholar] [CrossRef]
- Shan, Y.; Huang, Q.; Guan, D.; Hubacek, K. China CO2 Emission Accounts 2016–2017. Sci. Data 2020, 7, 54. [Google Scholar] [CrossRef]
- Chen, X.; Shuai, C.; Wu, Y.; Zhang, Y. Analysis on the Carbon Emission Peaks of China’s Industrial, Building, Transport, and Agricultural Sectors. Sci. Total Environ. 2020, 709, 135768. [Google Scholar] [CrossRef] [PubMed]
- Chen, H.; Chen, W. Carbon Mitigation of China’s Building Sector on City-Level: Pathway and Policy Implications by a Low-Carbon Province Case Study. J. Clean. Prod. 2019, 224, 207–217. [Google Scholar] [CrossRef]
- Shan, Y.; Guan, D.; Liu, J.; Mi, Z.; Liu, Z.; Liu, J.; Schroeder, H.; Cai, B.; Chen, Y.; Shao, S.; et al. Methodology and Applications of City Level CO2 Emission Accounts in China. J. Clean. Prod. 2017, 161, 1215–1225. [Google Scholar] [CrossRef]
- Zhang, N.; Luo, Z.; Liu, Y.; Feng, W.; Zhou, N.; Yang, L. Towards Low-Carbon Cities through Building-Stock-Level Carbon Emission Analysis: A Calculating and Mapping Method. Sustain. Cities Soc. 2022, 78, 103633. [Google Scholar] [CrossRef]
- Ding, Y. Research on Net Carbon Emission Estimation and Carbon Compensation of Rural Tourism Destinations from the Perspective of Carbon Neutrality—Case Studies of Hongcun in Southern Anhui and Dawei in Hefei. Ph.D. Thesis, Nanjing Normal University, Nanjing, China, 2015. [Google Scholar]
- Ge, J.; Luo, X.; Lu, J. Evaluation System and Case Study for Carbon Emission of Villages in Yangtze River Delta Region of China. J. Clean. Prod. 2017, 153, 220–229. [Google Scholar] [CrossRef]
- Luo, X. Study on Low-Carbon Ecological Evaluation System of Villages Based on Carbon Emission Accounting. Ph.D. Thesis, Zhejiang University, Hangzhou, China, 2017. [Google Scholar]
- Wu, Y.; Zhu, W.; Zhu, X.; Yu, H.; Suer·Abula, J. Construction and Spatial-Temporal Characteristic Analyses of the Carbon Atlas of Low-Carbon Villages in the Yangtze River Delta. South Archit. 2022, 1, 98–105. [Google Scholar] [CrossRef]
- Wu, Y.; Sun, Y.; Zhou, C.; Li, Y.; Wang, X.; Yu, H. Spatial—Temporal Characteristics of Carbon Emissions in Mixed-Use Villages: A Sustainable Development Study of the Yangtze River Delta, China. Sustainability 2023, 15, 15060. [Google Scholar] [CrossRef]
- Pulselli, R.M.; Maccanti, M.; Marrero, M.; Van Den Dobbelsteen, A.; Martin, C.; Marchettini, N. Energy Transition for the Decarbonisation of Urban Neighborhoods: A Case Study in Seville, Spain. WIT Trans. Ecol. Environ. 2018, 217, 893–901. [Google Scholar] [CrossRef]
- Van Den Dobbelsteen, A.; Martin, C.L.; Keeffe, G.; Pulselli, R.M.; Vandevyvere, H. From Problems to Potentials-the Urban Energy Transition of Gruž, Dubrovnik. Energies 2018, 11, 922. [Google Scholar] [CrossRef]
- Pulselli, R.M.; Marchi, M.; Neri, E.; Marchettini, N.; Bastianoni, S. Carbon Accounting Framework for Decarbonisation of European City Neighbourhoods. J. Clean. Prod. 2019, 208, 850–868. [Google Scholar] [CrossRef]
- TU Delft Maps Own CO2 Emissions in Detail. Available online: https://www.tudelft.nl/en/2021/tu-delft/tu-delft-maps-own-co2-emissions-in-detail (accessed on 4 April 2024).
- Liu, M.; Yang, L. Spatial Pattern of China’s Agricultural Carbon Emission Performance. Ecol. Indic. 2021, 133, 108345. [Google Scholar] [CrossRef]
- Ma, S.; Xu, X.; Li, C.; Zhang, L.; Sun, M. Energy Consumption Inequality Decrease with Energy Consumption Increase: Evidence from Rural China at Micro Scale. Energy Policy 2021, 159, 112638. [Google Scholar] [CrossRef]
- Wan, W.Y.; Zhao, X.Y.; Wang, W.J.; Xue, B. Analysis of Spatio-Temporal Patterns of Carbon Emission from Energy Consumption by Rural Residents in China. Acta Ecol. Sin. 2017, 37, 6390–6401. [Google Scholar] [CrossRef]
- Liu, J.; Li, S.; Ji, Q. Regional Differences and Driving Factors Analysis of Carbon Emission Intensity from Transport Sector in China. Energy 2021, 224, 120–178. [Google Scholar] [CrossRef]
- Intergovernment Panel on Climate Change (IPCC). 2006 IPCC Guidelines for National Greenhouse Gas Inventories; National Greenhouse Gas Inventories Programme; IPCC: Geneva, Switzerland, 2006; Volume 20. [Google Scholar]
- National Development and Reform Commission; Ministry of Ecology and Environment of the People’s Republic of China. Provincial Greenhouse Gas Inventory Preparation Guidelines (Trial); Beijing, China; 2011. Available online: http://www.ncsc.org.cn/SY/tjkhybg/202003/t20200319_769763.shtml (accessed on 29 April 2024).
- 2018 Urban and Rural Construction Statistical Yearbook. Available online: https://www.mohurd.gov.cn/gongkai/fdzdgknr/sjfb/tjxx/jstjnj/index.html (accessed on 4 April 2024).
- Zhu, X.; An, J.; Ma, L.; Chen, S.; Li, J.; Zou, H.; Zhang, Y. Effects of Different Straw Returning Depths on Soil Greenhouse Gas Emission and Maize Yield. Sci. Agric. Sin. 2020, 53, 977–989. [Google Scholar] [CrossRef]
- Xiao, Q.; Hu, Z.; Hu, C.; Islam, A.R.M.T.; Bian, H.; Chen, S.; Liu, C.; Lee, X. A Highly Agricultural River Network in Jurong Reservoir Watershed as Significant CO2 and CH4 Sources. Sci. Total Environ. 2021, 769, 144558. [Google Scholar] [CrossRef]
- Fong, W.K.; Sotos, M.; Doust, M.; Schultz, S.; Marques, A.; Deng-Beck, C. Global Protocol for Community-Scale Greenhouse Gas Emission Inventories—An Accounting and Reporting Standard for Cities. Available online: https://www.wri.org/research/global-protocol-community-scale-greenhouse-gas-emission-inventories (accessed on 29 April 2024).
- IPCC. Climate Change 2021: The Physical Science Basis. 2021. Available online: https://www.ipcc.ch/report/ar6/wg1/ (accessed on 29 April 2024).
- National Forestry and Grassland Administration, N.P.A. of China. National Forest Resources Smart Management Platform. Available online: http://www.stgz.org.cn/cas/login?service=http%3A%2F%2Fwww.stgz.org.cn%2Flogin.do (accessed on 4 April 2024).
- Ministry of Water Resources of the People’s Republic of China. Annual Hydrological Report P. R. China. 2020, No. 4. Available online: https://www.mwr.gov.cn/sj/tjgb/szygb/202107/t20210709_1528208.html (accessed on 29 April 2024).
- Ministry of Ecology and Environment of the People’s Republic of China. “Construction and Investment in Rural Domestic Waste Classification, Collection, Transportation and Treatment Projects” Technical Guidelines (Trial Implementation) 2012, 20. Available online: https://www.mee.gov.cn/gkml/hbb/bgth/201204/W020230201320792342321.pdf (accessed on 29 April 2024).
- Ministry of Water Resources of China. Agricultural Irrigation Water Quota: Maize 2021. Available online: https://www.swj.sz.gov.cn/zwfw/bmxx/pshjs/csjhysgl/content/post_8206499.html (accessed on 29 April 2024).
- Ministry of Water Resources of China. Agricultural Irrigation Water Quota: Wheat 2020. Available online: http://swj.sz.gov.cn/attachment/0/415/415900/6746661.pdf (accessed on 29 April 2024).
- Huang, X.; Ding, W.; Zhang, L. Experimental Discussion on the Method of Water Metering Based on Electricity Consumptions in Qingfeng County. Water Resour. Dev. Manag. 2021, 11, 15–18. [Google Scholar] [CrossRef]
- National Burean of Statistics. China Rural Statistical Yearbook—2021; National Burean of Statistics: Beijing, China, 2021.
- Ning, L.; Zhang, Z.; Cai, B.; Zhou, C. Research on China’s Regional and Provincial Electricity GHG Emission Factors in 2020. Environ. Eng. 2023, 42, 222–228. [Google Scholar]
- GB/T 2589-2020; General Rules for Calculation of the Comprehensive Energy Consumption. State Administration for Market Regulation: Beijing, China; Standardization Administration of China: Beijing, China, 2020.
- Li, F.; Zhang, X.; Huang, J.; Liu, B.; Gao, X.; Shi, Y.; Li, K. Greenhouse Gas Emission Inventory of Drinking Water Treatment Plants and Case Studies in China. Sci. Total Environ. 2024, 912, 169090. [Google Scholar] [CrossRef]
- Li, H.; Jin, Y.; Li, Y. Carbon Emission and Its Reduction Strategies during Municipal Solid Waste Treatment. China Environ. Sci. 2011, 31, 259–264. [Google Scholar]
- Zhao, Y.; Chen, W.; Xu, H.; Gao, W.; Cheng, C.; Zhang, X.; Liang, Y. Energy Conservation and Emission Reduction in Upgrading and Reconstruction of Urban Wastewater Collection and Treatment System. Water Wastewater Eng. 2019, 45, 42–54. [Google Scholar]
- He, Y. The Measurement and Application of Industry Complete Carbon Emissions. Stat. Res. 2012, 29, 67–72. [Google Scholar]
- Wu, F.L.; Li, L.; Zhang, H.L.; Chen, F. Effects of Conservation Tillage on Net Carbon Flux from Farm Land Ecosystems. Chin. J. Ecol. 2007, 26, 2035. [Google Scholar]
- Zhang, G.; Lu, F.; Huang, Z.; Chen, S.; Wang, X. Estimations of Application Dosage and Greenhouse Gas Emission of Chemical Pesticides in Staple Crops in China. Ying Yong Sheng Tai Xue Bao/J. Appl. Ecol. 2016, 27, 2875–2883. [Google Scholar]
- Lee, J.G.; Chae, H.G.; Cho, S.R.; Song, H.J.; Kim, P.J.; Jeong, S.T. Impact of Plastic Film Mulching on Global Warming in Entire Chemical and Organic Cropping Systems: Life Cycle Assessment. J. Clean. Prod. 2021, 308, 127256. [Google Scholar] [CrossRef]
- Dubey, A.; Lal, R. Carbon Footprint and Sustainability of Agricultural Production Systems in Punjab, India, and Ohio, USA. J. Crop Improv. 2009, 23, 332–350. [Google Scholar] [CrossRef]
- West, T.O.; Marland, G. A Synthesis of Carbon Sequestration, Carbon Emissions, and Net Carbon Flux in Agriculture: Comparing Tillage Practices in the United States. Agric. Ecosyst. Environ. 2002, 91, 217–232. [Google Scholar] [CrossRef]
- Qin, X.; Li, Y.; Lu, Z.; Pan, W. What Makes Better Village Economic Development in Traditional Agricultural Areas of China? Evidence from 338 Villages. Habitat Int. 2020, 106, 102286. [Google Scholar] [CrossRef]
- Ye, X.; Chuai, X. Carbon Sinks/Sources’ Spatiotemporal Evolution in China and Its Response to Built-up Land Expansion. J. Environ. Manag. 2022, 321, 115863. [Google Scholar] [CrossRef]
- Map of Carbon Emissions. Available online: https://www.ipe.org.cn/MapLowCarbon/LowCarbon.html?q=5 (accessed on 4 April 2024).
- Pulselli, R.M.; Broersma, S.; Martin, C.L.; Keeffe, G.; Bastianoni, S.; van den Dobbelsteen, A. Future City Visions. The Energy Transition towards Carbon-Neutrality: Lessons Learned from the Case of Roeselare, Belgium. Renew. Sustain. Energy Rev. 2021, 137, 110612. [Google Scholar] [CrossRef]
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