Mapping of Carbon Dioxide Emissions at Provincial Scale: A Case Study of Guizhou Province in Western China

doi:10.21203/rs.3.rs-3550329/v1

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Mapping of Carbon Dioxide Emissions at Provincial Scale: A Case Study of Guizhou Province in Western China

https://doi.org/10.21203/rs.3.rs-3550329/v1

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published 09 Sep, 2024

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Accurate spatial distribution of carbon dioxide (CO₂) emissions is essential information needed to peaking emissions and achieving carbon neutral in China. The aim of this study was to map CO₂ emissions with high spatial resolution at provincial scale and then explore the scale effect on mapping results. As an example, the spatiotemporal pattern and factors influencing CO₂ emissions were examined in Guizhou Province in Western China. With the proposed method, a reasonable spatial distribution of CO₂ emissions with high spatial resolution was obtained, which had accurate information on spatial details. The optimal resolution of CO₂ emissions at the provincial scale under high spatial resolution was approximately 90 m and 1,260 m. Emissions of CO₂ were spatially heterogeneous in Guizhou, with high emissions in centers of big cities that gradually spread and decreased from city centers. From 2009 to 2019, the spatial distribution of CO₂ emissions developed from agglomeration to dispersion. Areas of high carbon emissions decreased, those of medium carbon emissions increased, and many areas changed from no emissions to carbon emissions. Industrial land had the highest emissions, followed by commercial and transportation lands. Emissions from rural residential land in Guizhou were much higher than those from urban residential land. Over 10 years, changes occurred in the relation between interregional economic level of Guizhou and CO₂ emissions, with the relation changing from linear into an inverted U-shaped relation. The effect of industrial structure on CO₂ emissions decreased, and the linear increase between CO₂ emissions and the urban scale became more evident. The results of this study will contribute to accurate monitoring and management of carbon emissions in Guizhou, as well as provide support to formulate policies related to controls on carbon emissions in different regions.

Earth and environmental sciences/Climate sciences

Earth and environmental sciences/Environmental sciences

Carbon dioxide emissions

High spatial-resolution mapping

scale effect

spatial pattern

Guizhou Province

optimal resolution

land use types

Since 2009, China became the country with the highest carbon dioxide (CO₂) emissions in the past 10 years ¹. Carbon dioxide emissions related to energy and industrial processes reached approximately 9.3 billion tonnes in 2017, with China accounting for approximately 30% of global emissions ². The Chinese government has proposed reaching peak carbon emissions by 2030 and achieving carbon neutrality by 2060, and thus, key future tasks in China will be directed toward achieving those goals. Fossil energy use and industrial production processes are the main factors causing increases in CO₂ emissions in China and also globally ¹. To realize peak carbon emissions and achieve carbon neutrality, it is important to obtain accurate spatial distributions and then refine grid management of those emissions ^3–5.

The spatialization of CO₂ emissions is of great significance to regional carbon research, regional emission control policies, and low carbon spatial planning ^{3, 6, 7}. Information on the fine structure of spatial emissions can assist to find effective points of intervention to promote emissions reduction by showing how emissions change between regions, between urban centres of different types and between urban and rural populations ⁸. What’s more, the produced emission maps can be used to see which parts of the region have highest emissions, and from what sources, which provide data support for carbon emission control policies. Mapping CO₂ emissions has been explored at global, national, provincial, and even county-level scales ^{3, 5}. Spatial resolution of global carbon emissions has been examined at 5°, 1°, 0.5°, 0.25°, and 0.1° ^9–13, whereas spatial resolution of CO₂ emissions at national, regional and provincial scales ranges from 1.0 km to 10 km ^{8, 14–16}. High spatial-resolution mapping of CO₂ emissions at the provincial scale is extremely important and can provide support for control and management of CO₂ emissions. However, the grid size of CO₂ emissions at the provincial scale is relatively coarse at approximately 1 km or even larger, making it difficult to meet requirements for precise management of carbon emissions ^{4, 17}.

The mapping of CO₂ emissions mainly include two methods, which are bottom-up estimation method and downscaling method ^{15, 18–21}. The method of bottom-up estimation relies on mass emission sources with geographical location information. Carbon dioxide emissions are first accounted at the facility-level data source based on their energy consumptions. The emissions of each spatial grid cell are the sum of the emissions of various facility in the grid cell^{4, 22}. Sources of emissions include large power plants, polluting enterprises, and traffic, among others ^{15, 18}. The bottom-up method is relatively accurate for point source emissions, but the locations and their energy consumption information of these mass facility data at a provincial scale are difficult to obtain. In particular, the location and relevant data of some small facilities are easy to be omitted. In addition, the bottom-up method is difficult to extend through all sectors and regions. The mapping of carbon emissions from nonpoint source data with this method also usually requires use of auxiliary data, which have the limitations associated with downscaling method ⁴.

Due to the difficulty of obtaining mass facility data for bottom-up method, downscaling method becomes an alternative method for the mapping of CO₂ emissions. In this method, total carbon emissions of a region are first accounted at the regional level based on regional energy consumption and emission factors. Then the total CO₂ emissions are distributed to each spatial grid cell according to the relationship between emissions and spatial auxiliary data, so as to obtain the spatial distribution of emissions ^{8, 14, 23, 24}. Population density, nighttime light data, and other remote sensing data are widely used as spatial auxiliary data because they reflect human activities ^{17, 25}. Downscaling method is feasible easily and requires relatively less data with low cost, it has become widely used for developing gridded emission inventories, especially in large-scale regions ¹⁵. Downscaling method has great potential in mapping of CO₂ emissions with high spatial resolution at relative large-scale regions, but it still has some limitations. First, the correlations between CO₂ emissions and auxiliary data are incomparable under different sectors or land use types, which is easy to lead to a poor correlation on the fine-scale spatial resolutions ⁸. For example, for the catering service sector and residential energy use, similar nighttime light values do not indicate similar CO₂ emissions, which is also the case for transportation and industrial sectors. Among different emission sectors, the relationship between CO₂ emissions and auxiliary data is different. Using the same relationship between CO₂ emissions and auxiliary data in a region will lead to greater uncertainty. Second, auxiliary data do not accurately indicate the variability in intensity of emissions from point source s without additional information, such as power plants ¹⁷.

To address these limitations mentioned above, this study proposed a new downscaling method for high spatial-resolution mapping of CO2 emissions based on multisource data. First, CO₂ emissions from 17 fossil fuels burned in 47 socioeconomic sectors were individually accounted. Second, socioeconomic sectors were merged, and their emissions were allocated to corresponding land use types. Within each land use type, nighttime light data were used for fine-scale allocation of CO₂ emissions. Third, the scale effect and optimal spatial resolution of CO₂ emissions at the provincial scale were determined. Last, the spatiotemporal pattern and factors influencing CO₂emissions in the study area were analyzed. This study will provide data to help meet goals of peak carbon emissions and carbon neutrality in China.

2.1 Study area

Most studies in China focus on mapping CO₂ emissions in the east or some developed provinces, and less attention is paid to the economically less developed provinces in western China ^{3, 5}. Most provinces in western China are in the initial stage of rapid economic development, and their CO₂ emissions are also in a stage of rapid increase. To balance economic growth and control of carbon emissions in these regions in the future, it is important to understand temporal and spatial distribution patterns of carbon emissions. Thus, in this study, the western province of Guizhou was used as a case study.

Guizhou Province is in the hinterland of Southwest China, and it covers approximately 176,167 km² and includes nine cities (prefectures) and 88 county-level administrative divisions (Fig. 1). Guizhou Province is a transportation hub in Southwest China and an important part of the Yangtze River economic belt. With implementation of a western development strategy, the economy of Guizhou entered a stage of rapid development. By 2019, the regional gross domestic product (GDP) in the province reached 1,676.934 billion yuan, with an annual growth rate of 8.3% ²⁶. The growth rate ranked first in China for nine consecutive years and was first in China for three consecutive years. Acceleration of industrialization and urbanization has led to rapid growth in energy demand and a significant increase in carbon emissions. The contradiction between demand for rapid economic growth and control of CO₂ emissions is increasingly prominent in Guizhou Province. Guizhou Province is a typical area of “rich coal, lack of oil and less gas,” and thus, coal has long dominated energy consumption.

2.3 Carbon accounting

Carbon emissions in Guizhou Province were considered primarily from two aspects: energy-related emissions and industrial process-related emissions, which were based on the 2019 refinement to the 2006 IPCC Guidelines ²⁷. Carbon accounting was conducted for 2009 and 2019. The emission factors adopted the recommended values consistent with actual emissions in China, which are described in Shan et al. (2018). Energy-related carbon emissions were those emitted during the combustion of fossil fuels, which included emissions from 17 fossil fuels burned in 47 socioeconomic sectors in this study. Emissions were calculated according to the following equation:

$${\varvec{C}\varvec{E}}_{\varvec{i}\varvec{j}}={\varvec{F}\varvec{C}}_{\varvec{i}\varvec{j}}\times {\varvec{N}\varvec{C}\varvec{V}}_{\varvec{i}}\times {\varvec{C}\varvec{C}}_{\varvec{i}}\times {\varvec{O}\varvec{R}}_{\varvec{i}\varvec{j}}$$

where ${CE}_{ij}$ and ${FC}_{ij}$ are the CO₂ emissions and the consumption of fossil fuel type i in socioeconomic sector j, respectively; ${NCV}_{i}$ is the net calorific value of fossil fuel type i; ${CC}_{i}$ is the CO₂ emissions per unit of net heat generated by fossil fuel i; and ${OR}_{ij}$ is the oxidation rate during fuel combustion ²⁸. Fossil fuel consumption (${FC}_{ij}$) was obtained based on China’s energy statistical yearbooks and Guizhou’s statistical yearbooks in 2009 and 2019.

Forty-seven socioeconomic sectors are described in Shan et al. (2018), including farming, forestry, animal husbandry, fishery and water conservancy, coal mining and dressing, petroleum and natural gas extraction, ferrous metals mining and dressing, production and supply of electric power, steam and hot water, transportation, storage, post and telecommunication services, wholesale, retail trade, and catering services, other service sectors, urban resident energy usage, and rural resident energy usage, among others. The 17 fossil fuels included raw coal, cleaned coal, other washed coal, briquette, coke, coke over gas, other gas, other coking products, crude oil, gasoline, kerosene, diesel oil, fuel oil, other petroleum products, liquefied petroleum gas, refinery gas, and natural gas.

Carbon dioxide emissions from industrial processes are primarily due to physical and chemical reactions in production processes. Cement production is the main source of carbon emissions, accounting for approximately 75% of total CO₂ emissions from industrial production in China ²⁸. Therefore, in this study, emissions from the cement production process were determined according to the following equation:

$${\varvec{C}\varvec{P}}_{\varvec{k}}={\varvec{F}\varvec{C}}_{\varvec{k}}\times {\varvec{C}\varvec{F}}_{\varvec{k}}$$

where ${CP}_{k}$ is the CO₂ emissions during cement production and ${FC}_{k}$ is the activity factor in the carbon emissions accounting for cement production in year k, that is also the cement output. The activity factor was obtained from the official data set of the National Bureau of Statistics. The emission factor of cement production is ${CF}_{k}$, which was equal to 0.2906 ²⁸. Carbon dioxide emissions from cement production were classified as manufacturing in this study.

2.3 Mapping CO2 emissions

As the carrier of economic activities, land is also the carrier of CO₂ emissions from fossil fuel combustion and industrial production. Due to the difficulty of obtaining the location information of mass emission sources, this study presented a downscaling method for the mapping of CO2 emissions with the help of land use data, nighttime light data, and et al. The spatialization of CO₂ emissions in this study included three steps. First, the linkage of socioeconomic sectors and their corresponding land use type were established. The emissions for different sectors were allocated to corresponding land use types. Second, within each land use type, the total CO₂ emissions of each sector will be allocated to grid cells with size of 30 meters based on nighttime light data, population, and other spatial auxiliary data.

2.3.1 CO emissions from sectors to land use types

This study established the linkage of different sectors with land use types. The CO₂ emissions from different sectors were allocated to the corresponding land use types (Fig. 2). First, the 47 economic sectors were first merged into eight sectors, which including production and supply of electric power sector, the farming, forestry, animal husbandry, fishery and water conservancy sector, mining sector, transportation service sector, manufacturing industries, wholesale, retail trade, catering, and other service sectors, urban residents energy usage, and rural residents energy usage. The land use types in 2019 included mining land, industrial land, transportation sites, traffic and roads, urban residential land, rural settlements, and commercial land, among others. The spatial resolution of land use data is 30 meters, and the time is 2009 and 2019.

Carbon dioxide emissions from different industries were allocated to their corresponding land use types based on the land use classification system in 2019. The specific scheme is shown in Fig. 2. As point-source data, CO₂ emissions generated by power plants were allocated to industrial land parcels according to power plant coordinates. Data on power plants were obtained from the world power plant database (https://www.wri.org/research/global-database-power-plants). Carbon dioxide emissions from manufacturing were allocated to industrial lands excluding those where power plants were located. Emissions from combustion of oil fuels in transportation, storage, and post and telecommunication services sectors were allocated to roads, and emissions from combustion of nonoil fuels were allocated to transportation sites.

Carbon dioxide emissions from mining sectors were allocated to mining lands. Emissions from energy use by urban and rural residents were allocated to urban and rural residential lands, respectively. Emissions generated by wholesale, retail trade, catering services, and other service sectors were allocated to facility lands of the commercial service industry. Emissions from agriculture, forestry, animal husbandry, and sideline and fishery industries were allocated to lands for agricultural facilities, hydraulic construction, and other land types. Because the land use classification in 2009 does not separately list lands for the service industry, emissions from urban residents and the service industry were uniformly allocated to urban land.

2.3.2 High spatial resolution mapping of CO2 emissions in different land use types

After emissions of different industries were allocated to different land classes, emissions on each grid pixel cell were allocated adjusting for the weight of nighttime light or population in each land class ^{17, 25, 29}. The grid size was set to be consistent with the land use data, which is 30 meters. Carbon dioxide emissions in urban and rural residential lands were spatially distributed weighted by population data, whereas those from other land use types were spatially distributed weighted by lighting data. Emissions allocated increased as light or population values increased. Emissions were estimated according to the following equation:

$${\varvec{C}}_{\varvec{t}\varvec{j}}={\varvec{C}\varvec{E}}_{\varvec{j}}\times \frac{{\varvec{N}\varvec{L}}_{\varvec{t}}}{\sum _{\varvec{t}=1}^{\varvec{n}}{\varvec{N}\varvec{L}}_{\varvec{t},\varvec{j}}}$$

where ${C}_{tj}$ is the CO₂ emissions of grid t in ground class j; ${CE}_{j}$ is the total emissions allocated to land class j; ${NL}_{t}$ is the nighttime light brightness value or population value of the grid; n is the number of grid t in land type j; and $\sum _{t=1}^{n}{NL}_{t,j}$ is the sum of light values or populations of all grids in ground class j. Ultimately, spatial distributions of CO₂ emissions in 2009 and 2019 were obtained. Nighttime light and population data were resampled to a resolution of 30 m to be consistent with land use data. Nighttime lighting data used were obtained from the corrected global DMSP NTL time series data set ³⁰. Population data were from the kilometer-grid data set of the spatial distribution of China’s population from the Resource and Environmental Science and Data Center (https://www.resdc.cn/).

2.4 Scale effect analysis and optimal resolution selection

Scale dependence of spatial heterogeneity has always been a concern in geography and ecology, which includes grain and extent. Extent is the geographic scale, which was the provincial scale in this study. Grain represents the spatial resolution of a map. If the spatial resolution is too large, the location information of carbon emissions will not be expressed accurately. If the spatial resolution is too small, the mapping process will be time-consuming and the amount of data will increase, especially in a large area. Therefore, it is very important to determine the appropriate spatial resolution for the mapping of CO₂ emissions. In this study, the spatial distribution maps of CO₂ emissions with different resolutions were produced by changing the grid size. The scale effect and the optimal resolution of the spatial distribution of emissions under different resolutions were analyzed. At the highest spatial resolution, the minimum grid size is set to 30 m. The spatial distributions of CO₂ emissions under different grid sizes are obtained by increasing grid size at 30-m increments up to 2,010 m.

Referring to previous studies, this study used the landscape metrics to analyze the scale effect of the spatial distribution of carbon emissions and determine optimal resolution ^{3, 31}. The landscape metrics was calculated by FRAGSTATS 4.2 software ³². The changes and scale effects of the index at different scales were analyzed. Scale effect indicators were Shannon’s diversity index (SHDI) and evenness index (SHEI), the aggregation index (AI), and the proportion of like adjacencies (PLADJ), which are sensitive to scale changes ^{31, 33}. The two Shannon indices indicate diversity (SHDI) and evenness (SHEI) of patch types and therefore are measures of landscape heterogeneity. Both indices are especially sensitive to unbalanced distributions of each patch type in the landscape. The AI indicates the spatial aggregation of patch types, and PLADJ indicates the agglomeration of a landscape. All indicators were calculated at the landscape level. When a landscape index changes with scale, there may be an obvious “scale turning point (inflection point)”, and the two adjacent inflection points are called the scale domain. In a scale domain, the landscape pattern index is relatively stable, and therefore, its pattern characteristics are relatively stable, which can better reflect characteristics of the regional landscape pattern and also indicate optimal resolution.

2.5 Analysis on influencing factors of regional differences in CO emissions

Based on the spatial distribution of CO₂ emissions, this study further explored the influencing factors of regional differences in CO₂ emissions in the study area. The influencing factors mainly considered the economic development conditions, industrial structure, and urbanization level. With counties (districts) as the unit, CO₂ emissions, economic development conditions, industrial structure, and urbanization level were determined for 88 counties (districts) in Guizhou Province in 2009 and 2019. Economic development condition was indicated by GDP and per capita GDP. Industrial structure was indicated by proportions of primary, secondary, and tertiary industries. Area of built-up area, per capita construction land area, and proportion of construction land area were used to indicate urbanization level. The scatter diagram between each impactor factors and CO₂ emissions were constructed and their relationship were analyzed based on the statistical models, such as linear function, univariate quadratic polynomial, exponential function, logarithmic function, etc.

3.1 Carbon dioxide emissions accounting results

Carbon dioxide emissions in Guizhou Province increased from 184.7 million tonnes in 2009 to 258 million tonnes in 2019. Thus, rapid economic development of Guizhou over the 10 years led to an approximate increase of 73 million tonnes in CO₂ emissions, an increase of approximately 7.3 million tonnes per year. Carbon dioxide emissions under different economic sectors and land use types in Guizhou Province are shown in Table 1. The highest emissions were in production and supply of electric power sector, which reached 114.48 million tonnes in 2019 and accounted for 58.63% of total emissions. The second largest emitters of CO₂ were manufacturing sector, which emitted 49.74 million tonnes of CO₂ emissions. Therefore, the land use type with the highest emission was the industrial lands, which reached 164.22 million tonnes in 2019 and accounted for 63.65% of total emissions. Compared with 2009, emissions increased by 26.88 million tonnes. Rapid development of industrialization in Guizhou over the 10 years was the main reason for the increase. Therefore, how to reduce carbon emissions from industrial land is the primary problem that Guizhou currently needs to address. Owing to rapid development of tertiary industry in Guizhou Province, CO₂ emissions from wholesale, retail trade, catering, and other service sectors also increased significantly,, which reached 43.92 million tonnes and accounted for 17.03% of total emissions. Those service sectors were became the third largest emitters of CO₂ and their CO₂ emissions increased by 26.52 million tonnes over the 10 years of the study area. Carbon dioxide emissions from transportation land reached 16.19 million tonnes, which accounted for 6.28% of total emissions and was an increase of approximately 7.89 million tonnes compared with 2009 emissions.

Carbon dioxide emissions from rural residential energy usage reached 15.15 million tonnes in Guizhou Province, which was much higher than that of urban residential energy usage with emissions of 5.18 million tonnes. Compared with 2009, the growth of emissions from rural residential areas was much greater than that of urban residential land. The continuous increase in rural residential area in Guizhou was the primary reason for the difference, which was much greater than that in urban residential area. The result also indicates that Guizhou still needs to further accelerate the pace of urbanization and promote intensive economic development. In the future, identifying the best solution to balance accelerating urbanization and control of carbon emissions will be the major challenge for development of Guizhou. Emissions from agriculture, forestry, animal husbandry, and sideline fisheries did not increase significantly over the 10 years, whereas the only land type with reduced carbon emissions among all land types was mining land.

Table 1. Carbon dioxide emissions from different sectors and land use types in Guizhou (mt).

Emissions/Land use	Urban residential land	Rural residential land	Industrial land	Mining land	Land for transportation	Land for service sector	Land for agricultural and water facilities	Total
2009	2.9	6.7	137.3	9.4	8.3	17.4	2.8	184.8
Proportion	1.57%	3.63%	74.30%	5.09%	4.49%	9.42%	1.52%	100%
2019	5.18	15.15	164.18	8.13	16.17	43.92	5.21	257.9
Proportion	2.01%	5.87%	63.65%	3.15%	6.27%	17.03%	2.02%	100%
Growth	2.28	8.45	26.88	-1.27	7.87	26.52	2.41	73.1
Growth rate	78.62%	126.12%	19.58%	-13.51%	94.82%	152.4%	86.07%	78.6%

Note: URENU: urban residential energy usage, and RRENU: rural residential energy usage; EP: production and supply of electric power sector; Service sector: wholesale, retail trade, catering, and other service sectors; FFAHFW: the farming, forestry, animal husbandry, fishery and water conservancy sector.

3.2 Spatial patterns of carbon dioxide emission

Spatial distribution of CO₂ emissions in the 30-m grid is shown in Fig. 3. Carbon dioxide emissions in Guizhou Province in 2009 were spatially aggregated, and most emissions were concentrated in urban areas of various cities (prefectures). High carbon emission areas were primarily concentrated in Guiyang City and Zunyi City. However, in 2019, CO₂ emissions were more spatially diffuse and dispersed. Because of urban expansion, areas with CO₂ emissions increased significantly over the 10 years. Many areas changed from noncarbon emission areas to those with carbon emissions. Although distribution of medium carbon emission areas increased, that of high carbon emission areas decreased significantly. In addition, carbon emissions gradually spread from west to east. Thus, the spatial distribution of carbon emissions developed from agglomeration to decentralization in Guizhou, which reflected the trend toward decentralized and balanced development of industrial layout and expansion of urbanization.

To compare mapping results of carbon emissions under different grid sizes, spatial distributions of CO₂ under 54 grid sizes at 30-m increments were created (later, the increment was changed to 60 m and then to 90 m). The minimum grid size was 30 m, and the maximum was 2,010 m. Some of the results are shown in Fig. 4. In the 90-m grid, there was spatial heterogeneity in the spatial distribution of CO₂ emissions. The spatial heterogeneity of CO₂ emissions became more evident with increasing grid size to a certain point, but with further increases in grid size, spatial heterogeneity and details were reduced. Overall, CO₂ emissions from big city centers, such as Guiyang and Zunyi, were high but gradually diffused and decreased around those cities. Emissions from traffic and roads and rural areas were intermediate.

3.3 Scale effect and optimal resolution

Changes in spatial grid size significantly affected CO₂ mapping results. The optimal spatial resolution for high spatial-resolution mapping of CO₂ emissions at the provincial scale was a grid size of 90 m and 1,260 m (Fig. 5). According to SHDI and SHEI of different grid sizes at the landscape level, the finest grain was from 30 m to 90 m and from 1,140 m to 1,260 m, because curves of the two indices increased steadily within those ranges (Fig. 5A, B). The curve of AI values indicated the finest grain was from 30 m to 90 m, from 1,160 m to 1,260 m, and from 1,740 m to 1,830 m, according to the same criteria (Fig. 5C). The curve of PLADJ values indicated the most suitable grid size was from 30 m to 90 m and from 1,200 m to 1,290 m (Fig. 5D). The turning point and scale domain reflected the scale sensitivity and stability of landscape change. A small grid usually leads to a power exponential increase in the amount of data, resulting in a reduction in operational efficiency. Therefore, in a relatively stable scale domain, a relatively large grid should be selected as the optimal resolution. Thus, in Guizhou Province, based on the turning point and scale domain of the above landscape indices, the optimal resolution at high spatial resolution was 90 m, followed by 1,260 m. The appropriate resolution can be determined according to the actual application requirements.

3.4 Comparison with other gridded data

Spatial allocation results of this study were compared with spatial data of CO₂ emissions obtained by other methods, including the Regional Emission Inventory in Asia (REAS) with grid size of 0.25° ¹² and grid data in Peking University’s global pollutant emission inventory with grid size of 0.1°^{13, 14}. The spatial variation of the REAS and global pollutant emission inventory is similar to that in this paper (Fig. 6). In 2009, CO₂ emissions were relatively concentrated, and high carbon emission regions were primarily concentrated in the western region of Guizhou Province. By 2015, the distribution of CO₂ emissions was more balanced, with a significant decrease in high carbon emission areas, an increase in medium carbon emission areas, and carbon emissions gradually spreading from west to east. However, because of limitations in spatial resolution, more refined emission distributions, such as those associated with roads, could not be determined. Therefore, the REAS and global pollutant emission inventory cannot provide the more refined grid data needed to manage CO₂ emissions at the provincial level. The method proposed in this paper can obtain more precise spatial distribution of CO₂ emissions.

3.5 Analysis on influencing factors of regional differences in CO₂ emissions

With counties (districts) as the unit, CO₂ emissions, economic level, industrial structure, and urbanization level were determined for 88 counties (districts) in Guizhou Province in 2009 and 2019. Relations were analyzed between regional economic level, industrial structure, urbanization level, and CO₂ emissions based on spatial allocation of CO₂ emissions (Figs. 7–9). Economic level was indicated by GDP and per capita GDP. Industrial structure was indicated by proportions of primary, secondary, and tertiary industries. Area of built-up area, per capita construction land area, and proportion of construction land area were used to indicate urbanization level.

The relation between regional economic level and CO₂ emissions developed from a linear relation to an inverted U-shaped one in the 10 years from 2009 to 2019 (Fig. 7). In 2009, there were positive linear relations between GDP and per capita GDP and emissions in all counties of Guizhou Province (Fig. 7A, B). In 2009, Guizhou was in the initial stage of economic development. The GDP of each region was less than 300 billion yuan, and the per capita GDP was less than 40,000 yuan. Because economic development was strongly dependent on energy, as the economic level increased, CO₂ emissions in the region also increased. By 2019, relations between GDP and per capita GDP and CO₂ emissions of counties in Guizhou changed to an inverted U-shaped relation. Therefore, when the economic level was low, regional CO₂ emissions increased with an increase in economic level, but when the regional economy developed to a certain level, CO₂ emissions decreased with a further increase in economic level (Fig. 7C, D).

The relation between industrial structure and CO₂ emissions is shown in Fig. 8. The effect of regional industrial structure on CO₂ emissions weakened over the 10 years. Regional CO₂ emissions showed a significant exponential decline with the increase in proportion of primary industry, with an R² value of 0.7 and a decline rate of 2.472 in 2009. By 2019, there was still an exponential decline in the relation between CO₂ emissions and proportion of primary industry, but the R² value was less than 0.2 and the decline rate was 1.064 (Fig. 8A, D). Regions with a high proportion of primary industry had low degree of industrialization and urbanization, and those regions had less CO₂ emissions. The relations between proportion of secondary industry and carbon emissions showed exponential increases in both years, with R² values of 0.33 in 2009 and 0.29 in 2019 (Fig. 8B, E). There was no relation between proportion of tertiary industry and carbon emissions (Fig. 8C, F). The absence of a relation might be because some tertiary industries, such as transportation and the catering industry had high carbon emissions, whereas some had low carbon emissions, such as tourism and the high tech industry. Therefore, development of low-carbon emission industries should be considered in development of tertiary industries. Adjustment of industrial structure will be an important factor in the control of CO₂ emissions in Guizhou.

Over the 10 years of the study, the influence of urbanization level on CO₂ emissions in Guizhou Province increased. Carbon dioxide emissions increased with increasing area of regional built-up areas in both 2009 (R² = 0.5) and 2019 (R² = 0.7) (Fig. 9A, D). There was no significant relation between area of construction land per capita and carbon emissions (Fig. 9B, E). However, in large per capita construction areas (more than 2.7 km²/10,000 people in this study), that is, in areas with low population concentration, CO₂ emissions were low. Urbanization level and economic development of those areas were relatively low, and therefore, CO₂ emissions were not high. There was an inverted U-shaped relation between proportion of construction land in the total area and CO₂ emissions. Thus, the larger the proportion was, the higher the CO₂ emissions, until the proportion of construction land reached a certain degree. Then, with further increases in the proportion, CO₂ emissions decreased (Fig. 9C, F). Therefore, carbon emissions will increase with expansion of construction land in the developing stage of a region, but when the region has developed to a certain extent, continuous expansion of construction land will not lead to further increases in carbon emissions.

Remote sensing data such as nighttime light have great potential for the spatial distribution of CO₂ and other pollutant emissions ^{17, 23, 30}. Such data are easily accessible at low cost and are suitable for large-scale mapping of carbon emission ¹⁰. Effective utilization of remote sensing data will help to improve accuracy of mapping results and will benefit the obtaining of spatiotemporal characteristics of CO₂ emissions. In this study, a method was presented to spatialize CO₂ emissions at a provincial scale with high spatial resolution that combined data on nighttime light, populations, point sources data, and land use types. The approach is more reasonable than that of the previous method of space allocating that used auxiliary data in an administrative region ¹⁰. Human activities represented by the intensity of nighttime light values in the same land use type were comparable. Emissions of power plants were allocated to the industrial parcel where they were located according to their coordinates, which can avoid underestimation caused by mapping of emissions using nighttime lighting data. Vehicles are mobile and are affected by many factors, such as vehicle stock and activity levels. Therefore, emissions of vehicles are difficult to quantify and locate spatially ³⁴. Because nighttime lighting data provide good indication of the flow of vehicles on roads, emissions of crude oil, gasoline, diesel oil, fuel oil, and other petroleum products in the transportation service industry were allocated to road land with the help of nighttime light data in this study.

Land as the carrier of economic activities, CO₂ emissions from different sectors are reflected in different types of construction land ³⁵. In the method in this paper, a correlation was established between emissions and construction land types. Because of the linkage with land use type, CO₂ emissions can be updated while monitoring land use change. The details of a land use classification will affect the accuracy of spatial distributions of CO₂ emissions. For example, most land use data do not distinguish between urban residential land and commercial land, although the nighttime light value of residential land is inconsistent with that of commercial land in indicating spatial differences in carbon emissions. Only nighttime light values between similar industries are comparable. Therefore, the more detailed the land use classification is, the more accurate the spatial allocation of CO₂ emissions, or the coarser the land use classification is, the greater the uncertainty of spatial allocation of CO₂ emissions.

The downscaling method used in the mapping of CO₂ emissions assumed linear relations between CO₂ emissions and spatial proxy variables ^{8, 14, 23}. Previous studies suggest that underlying assumption is potentially valid at a coarse scale but is highly suspect at a fine scale ¹⁵. The main reason for the difference may be that a linear relation is assumed in a given district rather than in the same land use type or the same sector. When the resolution is high, proxy variables such as nighttime light easily reflect characteristics of different land uses. However, proxy data values are not comparable, and there are poor correlations between proxy variables and emissions. Zheng et al. (2017) also found that emissions were sensitive to proxy variables at finer scales in various sectors, especially for residential and transportation emissions. Therefore, it is reasonable to associate land uses with industry sectors and then allocate CO₂ emissions in different land uses with the help of selected proxies. In high spatial-resolution mapping of carbon emissions, auxiliary variables must be used with caution and cannot be used directly regardless of land type characteristics.

Development of economies in western provinces of China began relatively late, but the economy is now in a period of rapid growth. Guizhou’s CO₂ emissions are at an intermediate level in China, but its annual growth rate in recent years is relatively high. Emissions in 2009 and 2019 in Guizhou indicated the primary CO₂ emission source was the industry sector, especially production and supply of electric power. Thus, policy makers should require upgrades to emission control technologies of existing industries and consider increasing standards for industrial emissions ²². At the same time, Guizhou should give full play to its advantages of rich water resources, increase the utilization of clean energy and reduce the carbon emission of the production and supply of electric power.

Carbon dioxide emissions from rural residential lands in Guizhou Province were much higher than those from urban residential lands, and this gap is still increasing in recent 10 years. According to the land use map in 2019, the area of rural residential lands in Guizhou is about 4.5 times that of urban residential lands. Guizhou still needs to accelerate the pace of urbanization for a long time in the future. The new urbanization process can learn from the experience of the developed areas in the East of China and promote high-quality urbanization. In urban planning, land for various facilities should be rationally distributed, and the balance between work and housing should be advocated. For new houses, we should promote the use of new energy-saving materials and improve the level of central heating to improve energy efficiency. Moreover, the coverages of subways in various cities of Guizhou are limited due to the limitation of topography. At present, only two subways have been opened in Guiyang, the provincial capital of Guizhou province. The government needs to further accelerate the construction of subway and tram, so as to improve the level of public travel and reduce carbon emissions. In addition, reducing the energy consumption of rural residential areas and improving their utilization efficiency are also the focus of Guizhou in the future.

Some studies indicated that there was a linear positive relationship between regional economic and CO₂ ^{36, 37}, while some studies suggested that there was an inverted U-shaped relationship between them ^{38, 39}. The main reason may be that the economic level of some regions is still in the initial stage, which is far from reaching the inflection point of inverted U-shape. The empirical evidence of Guizhou indicated the relationship between urbanization and CO₂ emission inevitably changes from linear relationship to inverted U-shaped relationship. However, China’s plan to reach the peak of carbon emissions in 2030 inevitably needs to accelerate the transformation from linear relationship to U-shaped relationship under human intervention. How to balance the relationship between the growth of economic and the control of carbon CO₂ emission is particularly important, especially for the western region of China, where is still in the initial development stage.

Accurate spatial distribution of CO₂ emissions is essential information of refined grid management for controlling CO₂ emissions. This study proposed a new downscaling method for high spatial-resolution mapping of CO₂ emissions based on multisource data and then explored the scale effect of CO₂ emissions at a provincial scale. With Guizhou Province as an example, this study also produced hot spot maps of CO₂ emissions and explored the influence factors of regional differences in CO₂ emissions. Results showed that CO₂ emissions in Guizhou were spatially heterogeneous and agglomerated. Emissions in centers of big cities were relatively high, and emissions gradually diffused and decreased around those cities. Over the past 10 years, the distribution of CO₂ emissions developed from agglomerated to dispersed, areas of high carbon emissions decreased, areas of medium carbon emissions increased, many areas changed from no carbon emissions to carbon emissions, and carbon emission areas gradually spread from west to east. The distribution of CO₂ emissions in Guizhou reflected trends in industrial layout and urbanization expansion and the transition to decentralized development. Changes in spatial grid size significantly affected CO₂ emissions mapping results. The optimal resolution for high spatial-resolution mapping of CO₂ emissions at the provincial scale was 90 m and 1,260 m according to the landscape index of different grid sizes. The appropriate resolution can be determined according to the application requirements.

Carbon dioxide emissions in Guizhou were approximately 258 million tonnes in 2019, which was an increase of approximately 7.3 million tonnes per year in the 10 years from 2009 to 2019. Industrial land was the land use type with the highest emissions, accounting for 63.65% of total emissions. Therefore, how to reduce carbon emissions from industrial land is the primary task in reaching the goal of peak carbon emissions in Guizhou in 2030. The second largest emissions were from lands for the service industry, which accounted for 23.3% of total emissions. Carbon dioxide emissions from rural residential lands in Guizhou Province were much higher than those from urban residential lands, indicating that Guizhou still needs to accelerate the future pace of urbanization. How to balance accelerating urbanization with carbon emission control is a major challenge in the development of Guizhou. In the 10 years from 2009 to 2019, the relation between regional economic level and CO₂ emissions in Guizhou developed from a linear one to an inverted U-shaped one. In addition, there was a decrease in the effect of industrial structure on CO₂ emissions but an increase in that of urbanization.

The high spatial-resolution mapping of CO₂ emissions in this study could reflect differences in carbon emissions between different land use types and industries. Therefore, it can help to coordinate development of urbanization, optimize land space, and upgrade industries with the goal of carbon neutrality, as well as help to realize more refined management of carbon emissions. As an example, Guizhou Province demonstrated the necessity of understanding temporal and spatial characteristics of CO₂ emissions in underdeveloped western regions of China. The results of this study will provide basic support for overall national planning of policies addressing peak carbon emissions and also be a reference for studies on carbon emissions and scale effects in other regions.

Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Author Contributions statement

The study conception and design were performed by Canying Zeng and Shaohua Wu. Material preparation and data collection were performed by Min Cheng and Hua Zhou. Data analysis were performed by Canying Zeng, Min Cheng and Fanglin Li. The first draft of the manuscript was written by Canying Zeng and Shaohua Wu. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This study was funded by the National Natural Science Foundation of China (41901062), Natural Science Foundation of Zhejiang Province (LY22D010009), the China Postdoctoral Science Foundation (2018M642389).

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No competing interests reported.

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Mapping of Carbon Dioxide Emissions at Provincial Scale: A Case Study of Guizhou Province in Western China

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