Spatial–Temporal Pattern Characteristics and Impact Factors of Carbon Emissions in Production–Living–Ecological Spaces in Heilongjiang Province, China

Abstract

Under the threat of global climate change, China has proposed a dual carbon goal of peak carbon and carbon neutrality. As the vital carrier for territorial spatial planning, production–living–ecological (PLE) spaces drive carbon emissions and are important to the dual carbon goals. In this study, carbon emissions and sinks of PLE spaces in cities in Heilongjiang Province from 2005 to 2020 were calculated and spatial–temporal changes were analyzed. The carbon emission structure was analyzed in segmentation sectors. The land use changes and socioeconomic factors on carbon emissions were analyzed, and emission reduction strategies were implemented. The results show the following: (1) Carbon emissions from production and living spaces increased yearly. Carbon sinks were smaller than emissions, but capacity was stable. (2) Higher-emission cities were concentrated in southwest Heilongjiang, and carbon emission differences between regions gradually increased. (3) Among carbon emission sectors, agricultural and household made up smaller proportions, while animal husbandry, industrial, transportation, and traffic travel contributed most. Carbon emission structures were transformed by adjusting urban development and industrial structure. (4) For most cities, industrial space was the main emission space, but agricultural production and urban–rural living spaces dominated in some cities. (5) GDP, urbanization rate, and area of city paved roads suppressed emissions in cities with decreased carbon emission grades. The industrial structure and coal consumption inhibited emissions in cities with maintaining and increasing carbon emissions grades.

1. Introduction

Climate change has become a major challenge for society as a whole. Climate change caused by greenhouse gas emissions, especially carbon dioxide emissions, constantly reminds mankind to achieve carbon emission reduction commitments promptly [1,2,3]. In the past few decades, China has been the world’s largest CO₂ emitter, accounting for approximately 29% of global CO₂ emissions [4]. To alleviate the dual pressures of domestic environmental degradation and international climate negotiations, China announced greenhouse gas emission targets in the “U.S.–China Joint Announcement on Climate Change”, the core of which is a commitment to peak CO₂ emissions by 2030 or sooner [5]. In the 75th session of the United Nations General Assembly, China reaffirmed the “dual carbon goals” of peaking CO₂ emissions by 2030 and achieving carbon neutrality by 2060 [6]. China is in the development stage and faces the crises and challenges of tightening resource constraints, increasingly serious environmental pollution, and gradual degradation of ecosystems [7].

The planning concepts of “intensive and efficient production space, livable and moderate living space, and beautiful ecological space” proposed by China at the 18th National Congress are known as production–ecological–living (PLE) spaces and have become important means to implement the dual carbon goals [8]. PLE spaces not only indicate space, but also reflect their production, living, and ecological functions. A consensus has been reached regarding the classification of PLE spaces based on the multifunctionality of land use [9,10]. Many studies on urban functional space and multifunctionality have similarities with Chinese studies on land use classification [11,12,13], that is, they carry out studies related to the coordination and balance of regional production, ecological, and living space functions from the perspective of spatial planning and management. Recently, various studies on carbon emissions in PLE spaces have been conducted, including the construction of a theoretical framework [14], identification and classification of spatial functions [15,16,17], and evaluation and optimization strategies [18,19,20].

Since China put forward territorial spatial planning and the dual carbon goals, carbon emissions from territorial spaces have become a focal point of research. Xu et al. constructed a greenhouse gas accounting framework suitable for territorial spatial planning that solved the problem of carbon emission accounting in urban and central urban areas [21]; King et al. analyzed the distribution characteristics of carbon emissions in territorial space and compared the differences in each functional area [22,23,24]. Some scholars have also explored territorial spatial planning methods and optimization strategies of provincial, municipal, and county administrative units with the goal of carbon emission reduction [25,26,27]; some scholars have further studied the carbon emission reduction of territorial space through carbon balance zoning and carbon emission reduction capacity measurement [28]. For example, Feng et al. constructed a carbon metabolism network model in an urban PLE space, analyzed the changes in horizontal carbon flow, and evaluated the comprehensive effect of PLE space changes using an ecological network analysis method [29]. Guo et al. calculated the carbon emissions of PLE spaces in Chongqing and used the STIRPAT model to analyze influencing factors [30]. Commonly used carbon emission analysis methods include the LMDI model [31], STIRPAT model [32], and Kaya constant equation [33]; however, each method has a different research applicability. Duro et al. decomposed international inequalities in per capita CO₂ emissions into the Kaya equation to analyze its inequality components [34]. Liu et al. adopted STIRPAT to assess the effect of urban household energy consumption [35]. Yang et al. examined the carbon emissions of the transportation sector of the Yangtze River Economic Belt based on the LMDI model [36].

The existing research provides solid theoretical support and rich methods for this study. However, most studies have focused on carbon emissions from territorial space from a macro perspective. Regarding provincial carbon emissions, few studies have calculated and analyzed municipal cities in the same provincial area from the perspective of PLE spaces. Furthermore, both land use and socioeconomic factors impact carbon emissions, but few scholars have discussed this impact. The study of carbon emissions from PLE spaces can indicate the contribution of carbon emissions in each city more deeply for clearer comparison. Research from the perspectives of PLE spaces, land use, and segmentation sectors can compensate for these deficiencies. The STIRPAT model could add impact factors according to the situation of the research object. It is more conducive for policymakers carrying out planning to promote urban carbon neutrality while simultaneously considering the overall characteristics of the province.

Heilongjiang Province is the main grain-producing area in China and is crucial to national food security. Moreover, Heilongjiang Province was an early industrial base. Upgrading energy and industrial structures is an important step in achieving the dual carbon goals. Population shrinkage has increased the challenge of achieving competitive development in Heilongjiang Province [37]. There are certain particularities in low-carbon development in other provinces and cities.

Therefore, this study takes Heilongjiang Province as an example, classifies the PLE spaces, constructs a calculation system for PLE space carbon emissions, and calculates emissions for each city. Subsequently, the spatial and temporal differentiation characteristics were analyzed. On this basis, the subdivision of carbon emission sectors was used to further analyze the main carbon sources and carbon emission structures of each city. This was followed by an analysis of the impact of land use change on carbon emissions. Finally, combined with the development characteristics of cities in Heilongjiang Province, a STIRPAT model was constructed to explore the factors influencing carbon emissions. The results provide a planning basis and scientific reference for territorial spatial planning and carbon emission reduction in Heilongjiang Province. The technical roadmap is shown in Figure 1.

2. Study Area and Methodologies

2.1. Study Area and Data Sources

2.1.1. Study Area

Heilongjiang Province is located in Northeast China, adjacent to eastern Russia, bordering eastern Mongolia, and is the northernmost and easternmost provincial administrative region (Figure 2), with an area of 473,000 km², accounting for 4.9% of China’s total land area [38]. Heilongjiang Province is the primary energy industry base in China and contributes a large amount of carbon emissions. Additionally, the province is the main granary ensuring China’s food security, and protecting its cultivated land areas is of great significance to strategic national development. Heilongjiang Province governs 13 prefecture-level administrative regions, including 12 prefecture-level cities and 1 prefecture. Owing to data limitations, the study area included the 12 prefecture-level cities and excluded Daxing’anling prefecture.

2.1.2. Data Sources

The land use data for 2005, 2010, 2015, and 2020 used in this study were obtained from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences (http://www.resdc.cn/) (accessed on 20 October 2022). The administrative boundary data for Heilongjiang Province were obtained from the National Geographic Information Resource Catalog Service System (https://www.Webmap.cn) (accessed on 20 October 2022). Statistically related data on energy consumption and economy were derived from or calculated based on the yearbooks of national, provincial, and local cities. The conversion coefficients of various energy sources were obtained from the China Energy Statistics Yearbook.

2.2. Classification System for Production–Living–Ecological Spaces

The construction of a scientific and reasonable PLE space classification system is the premise and basis for studying the characteristics and influencing factors of PLE space carbon emissions [39]. The territorial spatial pattern is a comprehensive reflection of the coupling of and interactions between natural ecological processes and human social systems, which perform a variety of functions [40,41,42,43]. Scholars have conducted extensive research on the classification of PLE spaces, mainly based on the dominant functions of different land use types [44]. Based on previous studies [45,46,47], this study considered the versatility of territorial space as the starting point, combined the land use classification system of the Chinese Academy of Sciences and the classification of land use status (GB/T21010-2007), classified the PLE spaces in the study area, and constructed a classification system (

Table 1. The PLE space classification system of Heilongjiang Province.

Primary Classification	Secondary Classification	Corresponding Land Use Type
Production space	Agricultural Production Space (APS)	Paddy field, dry land
	Industrial Production Space (IPS)	Other construction land
Living space	Urban and Rural Living Space (URLS)	Urban land and rural settlement
Ecological space	Forestland Ecological Space (FES)	Forestland, shrub area, wood land, other forest land
	Grassland Ecological Space (GES)	High coverage grassland, medium coverage grassland, low coverage grassland
	Water Ecological Space (WES)	River and canals; lakes; reservoir, pit, and ponds; bottom land
	Other Ecological Space (OES)	Swampland, bare soil, bare rock

).

2.3. Methods

2.3.1. Calculation of Carbon Emissions in PLE Spaces

In PLE spaces, production and living spaces are the main carbon sources, whereas the ecological space is the carbon sink. The carbon emissions and sinks of each space were calculated separately, and the carbon emissions of Heilongjiang Province were summarized. The specific formula is as follows:

$$C{E}_{PLE Space}=C{E}_{PS}+C{E}_{LS}-C{E}_{ES}$$

(1)

where $C{E}_{PLE Space}$ represents the total carbon emissions (CE) of the PLE space, $C{E}_{PS}$ represents the carbon emissions of the production space, $C{E}_{LS}$ represents the carbon emissions of the living space, and $C{E}_{ES}$ represents the carbon sink in the ecological space.

• Production spaces

The carbon emissions from the production space mainly originate from agricultural production spaces and industrial production spaces. Carbon emissions from APS include agricultural production activities and animal husbandry. Carbon emissions from agricultural production activities include agricultural fertilization, crop cultivation, agricultural machinery use, irrigation, and CH₄ emissions from paddy fields. Carbon emissions from animal husbandry include CO₂ emissions from livestock respiration and CH₄ emissions from livestock and poultry intestinal fermentation and feces. The primary livestock and poultry breeding varieties in Heilongjiang Province are pigs, cattle, sheep, and poultry [48]. CH₄ emissions must be converted into carbon emissions. According to the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report, the greenhouse effect induced by 1 ton of CH₄ is equivalent to that induced by 6.8182 tons of carbon [49,50,51,52]. The formula for calculating agricultural carbon emissions is as follows:

$$C{E}_{APS}={{\displaystyle \sum }}_{i=1}^n{T}_i\times {\alpha }_i$$

(2)

where CE denotes the carbon emissions in APS, T_i is the carbon emissions from each different APS source, and α_i is the coefficient for each APS carbon source.

Carbon emissions from IPS include industry and transportation. Industrial carbon emissions were calculated using the estimation method proposed by the IPCC in 2006 and 2019 and the carbon emissions of 15 end-use energy sources, including raw coal, coke, natural gas, diesel, fuel oil, liquefied petroleum gas, petroleum coke, gasoline, heat, and electricity [53]. The formula for the calculations is as follows:

$$C{E}_{Industry}={{\displaystyle \sum }}_{i=1}^n{E}_i\times NC{V}_i\times E{F}_i\times O_i$$

(3)

where $C{E}_{Industry}$ represents industrial carbon emissions; i represents energy type; E_i represents energy consumption; NCV_i represents average low-calorific value, that is, the heat released per unit of fossil energy combustion; EF_i represents emissions coefficient, that is, the total carbon emissions per unit net calorific value generated by burning fossil fuels; and O_i represents carbon oxidation rate, that is, the oxidation rate of burning fossil fuels.

According to production and life functions, transportation can be divided into two parts: transportation outside the living space and transportation inside the living space.

The former portion of carbon emissions belongs to the production space which includes passenger transportation and freight transportation [54]. The latter is residents’ travel using different modes of transportation. To distinguish this from the transportation of the production space, the inside part is called traffic travel. Typically, it is difficult to compare the two types of transportation [55]. Therefore, passenger turnover was converted into freight turnover, and transportation carbon emissions were calculated. The relevant parameters are listed in

Table 2. Transport passenger and freight conversion coefficients and carbon emission coefficients [54,55].

Transportation Mode	Transport Passenger and Freight Conversion Coefficients (ton/Person)	Carbon Emission Coefficients (kgCO2/t·km)
Railways	1	0.327
Highways	0.1	0.028
Water transportation	0.3	0.053
Civil aviation	0.072	1.961

, and the specific formula is as follows:

$$C{E}_{Transportation}={{\displaystyle \sum }}_{i=1}^n\left(T_{Passenger}\times {\alpha }_i+T_{Freight}\right)\times {\beta }_i$$

(4)

In the formula, $C{E}_{Transportation}$ represents transportation carbon emissions, $T_{Passenger}$ represents passenger turnover, ${\alpha }_i$ represents the passenger and cargo conversion coefficient, $T_{Freight}$ represents passenger turnover, ${\beta }_i$ represents the carbon dioxide emission coefficient of the ith transportation mode, and i represents the type of transportation. Transportation in this study included railways, highways, water transportation, and civil aviation and did not involve pipeline or other transportation modes.

The formula of carbon emissions in production is as follows:

$$C{E}_{PS}=C{E}_{APS}+C{E}_{Industry}+C{E}_{Transportation}$$

(5)

2.

Living spaces

Carbon emissions from living spaces comprise resident energy consumption and traffic travel. The carbon emissions from residential energy consumption were calculated using the electricity, natural gas, and liquefied petroleum gas consumed by residents. The specific formulas and coefficients refer to industrial carbon emissions. Carbon emissions from traffic travel were calculated based on ownership of private vehicles, buses, and taxis; average annual mileage; and carbon dioxide emission coefficient [56,57]. The relevant parameters are listed in

Table 3. Carbon emission coefficients of travel mode and average annual mileage [56,57].

Travel Mode	Carbon Emission Coefficients/kg/100 km	Average Annual Mileage/104 km
Private vehicles	22.3	1.5
Bus	88.1	6.5
Taxi	28.3	10

, and the specific formula is as follows:

$$C{E}_{LS}=C{E}_{REC}+{{\displaystyle \sum }}_{i=1}^3{Q}_i\times L_i\times {\beta }_i$$

(6)

where $C{E}_{LS}$ represents the carbon emissions from living space, $C{E}_{REC}$ represents the carbon emissions from resident energy consumption, and i represents different traffic travel modes, namely, private vehicles, buses, and taxis. Q_i represents vehicle ownership of the i th means of travel mode, L_i represents the average annual mileage of the i th means of travel mode, and β_i represents the carbon emission coefficients of the i th means of travel mode.

3.

Ecological spaces

As the main carbon sinks, ecological spaces play an important role in the absorption of carbon emissions. The carbon sink capacity of an ecological space was calculated using carbon sink coefficients and areas of different land use types. Ecological spaces include forest ecological spaces, grassland ecological spaces, water ecological spaces, and other ecological spaces. The study area comprises a temperate coniferous and broadleaf mixed forest and temperate meadow steppe. The carbon sink coefficients of the FES and GES were 0.585 tC/hm²/a and 0.021 tC/hm²/a, respectively. Considering previous research on water carbon sinks in China and its northeastern region, the adopted WES carbon sink coefficient was −0.0395 tC/hm²/a [58,59,60,61]. OES refers to barren mountain gravel land, sandy land, bare land, and other difficult-to-use land. Its carbon emissions and absorption capacity are relatively weak, and the carbon absorption rate changes little with geographical location. According to previous research [62], the carbon sink coefficient of OES is 0.005 tC/hm²/a. The ecological space carbon sink equation is as follows:

$$C{E}_{ES}={{\displaystyle \sum }}_{i=1}^4{A}_i\times {\theta }_i$$

(7)

where CE_ES represents the carbon sink of an ecological space; i represents different ecological spaces, namely, FES, GES, WES, and OES; $A_i$ represents the area of i th ecological spaces; and ${\theta }_i$ represents the carbon sink coefficient of the i th ecological space.

2.3.2. Spatial Autocorrelation

The spatial autocorrelation index can be used to measure the spatial aggregation or dispersion of regional carbon emissions. Autocorrelation analysis includes global and local spatial autocorrelation [63,64,65]. A global index was used to detect the spatial pattern of the entire region, and a single value was used to reflect the spatial autocorrelation of carbon emissions in the region. The global autocorrelation index is calculated as follows:

$$\mathrm{I}=\frac{{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{n}}{{\displaystyle \sum }}_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{ij}}\left({\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}\right)\left({\mathrm{x}}_{\mathrm{j}}-\overline{\mathrm{x}}\right)}{\left[{{\displaystyle \sum }}_{\mathrm{i}=1}^{\mathrm{n}}{{\displaystyle \sum }}_{\mathrm{j}=1}^{\mathrm{n}}{\mathrm{W}}_{\mathrm{ij}}{\left({\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}\right)}^2\right]}$$

(8)

where W_ij is the spatial weight matrix, x_i and x_j are the attribute values of spatial units, x is the average value, and n is the total number of cities. The global Moran index I ranges from −1 to 1. When I < 0, the distribution of the spatial elements is negatively correlated, and when I is closer to −1, the negative correlation is stronger. When I > 0, the distribution of the spatial elements is positively correlated, and when I is closer to 1, the positive correlation is stronger. When I = 0, the spatial elements are randomly distributed in space and there is no spatial correlation.

2.3.3. STIRPAT Model

A comparison of the three commonly used models for the analysis of carbon emission impact factors reveals that the factors in the decomposition of Kaya identity need to be analyzed year by year or by time and are constrained by the necessary identity. The LMDI method achieved no residual decomposition and quantified the contribution rate of each influencing factor in a specific year. However, the elasticity of each factor could not be examined; that is, when other factors remained unchanged, the change in carbon emissions caused by the change in one factor could not be determined. However, the traditional STIRPAT model includes only three factors—population, economic development level, and technical variables—that cannot comprehensively describe the impact of various socioeconomic aspects on CO₂ emissions. In contrast to the Kaya constant equation and LMDI model, STIRPAT allows the impact of each factor to be estimated as a parameter, and researchers can extend the model for their own research purposes [66,67,68,69].

The STIRPAT model is an extensible stochastic environmental impact assessment model based on the IPAT model. As a nonlinear random model with multiple independent variables, the STIRPAT model can incorporate appropriate independent variables according to conditions specific to each location when conducting carbon emissions research. The IPAT model is intuitive and concise, but the STIRPAT model overcomes the limitations of the proportional relationship between influencing factors and dependent variables and increases its flexibility and adaptability using the following formula:

I = a P^b A^c T^d e

(9)

where I, P, A, and T represent environmental pressure, population, affluence, and technical level, respectively. a is the model coefficient; b, c, and d are the exponential parameters of P, A, and T, respectively, and e is the random error of the model.

The equation is converted to logarithmic form as follows:

ln I = ln a + b ln P + c ln A + d ln T + ln e

(10)

To comprehensively and systematically analyze the factors influencing carbon emissions, gross regional product (GDP), industrial structure (IS), urbanization rate (UR), coal consumption (CC), area of city paved roads (CPR), per capita disposable income of urban residents (DIU), highway mileage (HM), construction land area (CLA), and total power of agricultural machinery (PAM) were added and expanded into the model. Because of the population loss in Heilongjiang Province, the urbanization rate was used to represent the population factor. The STIRPAT model was as follows:

$$\ln CE=\ln a+{\mathrm{b}}_1\ln GDP+{\mathrm{b}}_2\ln IS+{\mathrm{b}}_3\ln UR+{\mathrm{b}}_4\ln CC+{\mathrm{b}}_5\ln CPR+{\mathrm{b}}_6\ln DIU+{\mathrm{b}}_7\ln HM+{\mathrm{b}}_8\ln CLA+{\mathrm{b}}_9\ln PAM+\ln e$$

(11)

where a is the model coefficient; b₁, b₂, b₃, b₄, b₅, b₆, b₇, b₈, and b₉ are exponential parameters; and e is the random error of the model.

3. Results

3.1. Results of Carbon Emissions in PLE Spaces

3.1.1. Characteristics of Production Space Carbon Emissions

For the results of production space carbon emissions, the Jenks Natural Breaks method was used to divide that of 12 cities into five grades, namely, high, higher, medium, lower, and low grade (Figure 3). The same grading method was used in living space, ecological space, and PLE space.

The production space is the main carbon source in Heilongjiang Province. Except for Jixi and Mudanjiang, which showed a downward trend in carbon emissions, the other cities showed upward trends. The city with the highest production space carbon emissions was Daqing, which maintained a high-grade status through the entire study period, followed by Harbin and Qiqihar, which also maintained higher grades, and Heihe, which maintained a low grade. The carbon emission grades of the production spaces in other cities changed, among which Mudanjiang and Jixi changed from medium to lower grades in 2010 and 2020, respectively. Jiamusi was at a lower grade in 2005, changed to a medium grade in 2010 and 2015, and returned to a lower grade in 2020. Yichun was in a low grade from 2005 to 2015 and changed to a lower grade in 2020. Carbon emissions from the production space were gradually transferred from the north of Heilongjiang Province to the southwest.

3.1.2. Characteristics of Living Space Carbon Emissions

The carbon emission capacity of living spaces was slightly weaker than that of production spaces, but the production space of each city increased annually (Figure 4). Harbin has always been the city with the highest living space carbon emissions, high grade, and a high growth rate. With improvements in living standards and increased numbers of motor vehicles, the living space carbon emissions of Harbin may exceed those of the production space in the future, making it the first city in Heilongjiang Province whose living space is the main carbon emissions source. Daqing had the next highest grade. Hegang and Heihe remained in lower grades through the whole study period. Other cities changed to varying degrees. Qiqihar had a higher grade from 2005 to 2015 and transformed to a medium grade in 2020. While it had a higher grade in 2015, Mudanjiang remained at a medium grade in 2005, 2010, and 2020. Qitaihe changed from a lower grade to a low grade. Yichun, Jiamusi, Shuangyashan, Jixi, and Suihua changed between low, lower, and medium grades and finally stabilized at lower grades. Living space carbon emissions changed from a higher overall grade to a higher grade in the southwest and a lower grade in other regions.

3.1.3. Characteristics of Ecological Space Carbon Sinks

Carbon emissions from Heilongjiang Province were much higher than fixation in carbon sinks. The carbon sinks of cities other than Qiqihar, Jixi, Hegang, Daqing, and Mudanjiang showed downward trends, but the magnitude of variation was small (Figure 5). Overall, the carbon sink grade of the ecological space was relatively stable. Heihe contributed the highest carbon sink, followed by Yichun and Harbin, Jixi, and Shuangyashan. Cities with lower-grade sinks included Suihua, Hegang, and Jiamusi. Among these, Hegang was in a lower grade in 2005, transformed into a medium grade in 2010 and 2015, and returned to a lower grade in 2020. The cities with the weakest carbon sink capacities were Qiqihar, Daqing, and Qitaihe; a low grade was maintained during the study period.

3.1.4. Characteristics of PLE Space Total Carbon Emissions

There was little change in high- and low-carbon emission grades and more changes in medium-carbon emission grades (Figure 6). Daqing and Harbin had high total carbon emissions, Qiqihar had a higher grade, Heihe had a low grade, and Qitaihe had a medium grade. Hegang, Jiamusi, and Mudanjiang remained in the lower grades through the study period. The total carbon emissions grade of Jixi changed from medium to lower in 2020. Shuangyashan and Suihua changed from low to medium grades. Yichun changed from a low grade to a lower grade. Cities with higher total carbon emissions in Heilongjiang Province were more concentrated in the southwest, and the total carbon emissions of cities in other regions were relatively low.

3.2. Spatial–Temporal Characteristics of PLE Space Carbon Emissions

According to the results of the Moran’s I test in

Table 4. Moran’s I index of PLE space carbon emissions in Heilongjiang Province from 2005 to 2020.

	Moran’s Index	Z-Score	p-Value
2005	0.171999	1.597066	0.110251
2010	0.148051	1.415742	0.156851
2015	0.186807	1.688697	0.091277
2020	0.179023	1.688187	0.091375

, the Moran’s I index of total carbon emissions from 2005 to 2020 fluctuates greatly, first showing a decreasing trend, then increasing, and finally decreasing, from 0.171999 in 2005, 0.148051 in 2010, 0.186807 in 2015 to 0.179023 in 2020. Moran’s I index was greater than 0. However, the Moran’s I index in 2005 and 2010 did not pass the test with the absolute value of Z greater than 1.65 and the p-value less than 0.1, which indicates that the spatial correlation of carbon emissions in PLE spaces in Heilongjiang Province was weak and did not show significant aggregation during 2005–2010. The Moran’s I index in 2015 and 2020 passed the test with the absolute value of Z greater than 1.65 and the p-value less than 0.1, indicating that the spatial correlation is slightly stronger in 2015–2020, and there is a positive spatial agglomeration phenomenon. The overall difference in carbon emissions between the regions gradually widened.

3.3. Structural Features of Carbon Emissions in Segmentation Sectors

To further study the structure of carbon emissions, they were subdivided into six sectors: agricultural carbon emissions (ACE), animal husbandry carbon emissions (AHCE), industrial carbon emissions (ICE), transportation carbon emissions (TCE), household carbon emissions (HCE), and traffic travel carbon emissions (TTCE). The structure of carbon emissions and the contribution rate of each subsector were determined.

ACE and HCE accounted for a relatively small share of the subdivided sectors (Figure 7). The carbon emission structures of the five cities changed. In Harbin, the proportions of ACE, AHCE, and ICE decreased annually, and the largest contributing sector changed from ICE to TTCE. Furthermore, the ICE sector became the second largest contributor to carbon emissions. The carbon emissions structure of Shuangyashan changed from being dominated by ICE and AHCE to ICE alone. In Mudanjiang, the largest contributor changed from ICE to both AHCE and ICE. However, the leading carbon emission sectors in the other eight cities did not change. In Qiqihar, Jixi, Hegang, Daqing, and Qitaihe, ICE was the sector with the largest proportion of carbon emissions. Yichun and Jiamusi maintained the joint dominance of AHCE and ICE. The main sector of carbon emissions in Heihe was AHCE.

3.4. Impact Factors of Carbon Emissions in PLE Spaces

3.4.1. Impact Factors of PLE Space Transfer on Carbon Emissions

From Figure 8, it can be observed that APS and FES occupied a large proportion, and GES, WES, URLS, IPS, and OES occupied relatively small proportions. GES and OES appeared to decrease in space, and IPS and URLS appeared to increase in proportion. In the process of space change, FES in cities with strong carbon sink capacity was reduced. Combined with the carbon emissions of the PLE spaces, APS, IPS, and URLS were the main carbon sources, and construction land was the main carbon source. Figure 9 presents the carbon emissions of APS, IPS, and URLS. APS was the main carbon source in Heihe, and the main industries were agriculture and animal husbandry. IPS and URLS were the main carbon sources in Harbin, which was the only city in Heilongjiang Province where URLS contributed a large proportion of carbon emissions. The differences in carbon emissions contributed by APS, URLS, and IPS in Mudanjiang were not significant. For the other nine cities, including Qiqihar, IPS was the main carbon source. Even in Daqing, where GES had a larger share, FES was the main carbon sink in the city.

From the perspective of land use space, seven cities had increased APS areas: Harbin, Qiqihar, Shuangyashan, Daqing, Yichun, Jiamusi, and Heihe. Harbin, Shuangyashan, Yichun, and Jiamusi had reduced APS carbon emissions, indicating that green agriculture achieved its initial results in these cities. The other five cities with reduced APS areas were Jixi, Hegang, Qitaihe, Mudanjiang, and Suihua, among which Jixi and Hegang had increased APS, indicating that the implemented effects of green agriculture in these two cities were not significant. The IPS area and IPS carbon emissions of the ten cities other than Qitaihe and Mudanjiang increased, indicating that the increase in IPS area promoted carbon emissions. The IPS areas of Qitaihe and Mudanjiang also increased, but their IPS carbon emissions decreased, indicating that industrial structure upgrades in the cities had a mitigating effect on carbon emissions. The URLS area and URLS carbon emissions of each city increased; thus, increasing URLS area will increase carbon emissions.

3.4.2. STIRPAT Model Results

From the results of the STIRPAT model in

Table 5. STIRPAT Model Results.

City	STIRPAT Model	R2	Sig.
Harbin	$\ln CE=0.130\ln$$GDP-0.101\ln$$IS-0.089\ln$$UR+0.114\ln CC+0.117\ln$$CPR+0.129\ln$$DIU+0.076\ln HM+0.131\ln CLA+0.131\ln PAM$	0.978	0.013
Qiqihar	$\ln CE=0.091\ln$$GDP+0.118\ln$$IS-0.039\ln$$UR+0.254\ln CC+0.062\ln$$CPR+0.253\ln$$DIU+0.206\ln HM-0.007\ln CLA+0.083\ln PAM$	0.980	0.022
Jixi	$\ln CE=0.065\ln$$GDP+0.211\ln$$IS-0.191\ln$$UR+0.137\ln CC-0.222\ln$$CPR-0.109\ln$$DIU-0.090\ln HM-0.108\ln CLA-0.125\ln PAM$	0.922	0.033
Hegang	$\ln CE=0.094\ln$$GDP+0.041\ln$$IS+0.041\ln$$UR+0.043\ln CC+0.192\ln$$CPR+0.197\ln$$DIU+0.191\ln HM+0.204\ln CLA+0.060\ln PAM$	0.985	0.018
Shuang-yashan	$\ln CE=0.284\ln$$GDP+0.074\ln$$IS+0.014\ln$$UR+0.296\ln CC+0.047\ln$$CPR+0.154\ln$$DIU+0.061\ln HM+0.046\ln CLA+0.064\ln PAM$	0.965	0.025
Daqing	$\ln CE=0.130\ln$$GDP-0.073\ln$$IS-0.056\ln$$UR+0.163\ln CC+0.136\ln$$CPR+0.132\ln$$DIU+0.083\ln HM+0.129\ln CLA+0.199\ln PAM$	0.975	0.018
Yichun	$\ln CE=0.167\ln$$GDP-0.017\ln$$IS-0.021\ln$$UR-0.038\ln CC+0.105\ln$$CPR+0.190\mathrm{n}$$DIU+0.190\ln HM+0.081\ln CLA+0.187\ln PAM$	0.982	0.046
Jiamusi	$\ln CE=0.143\ln$$GDP-0.019\ln$$IS-0.089\ln$$UR+0.142\ln CC+0.147\ln$$CPR+0.151\ln$$DIU+0.042\ln HM+0.138\ln CLA+0.167\ln PAM$	0.964	0.036
Qitaihe	$\ln CE=0.300\ln$$GDP+0.193\mathrm{n}$$IS+0.120\ln$$UR+0.321\ln CC-0.171\ln$$CPR-0.147\ln$$DIU-0.086\ln HM-0.139\ln CLA+0.209\ln PAM$	0.968	0.032
Mudanjiang	$\ln CE=0.124\ln$$GDP+0.382\ln$$IS-0.152\ln$$UR+0.152\ln CC-0.090\ln$$CPR+0.068\ln$$DIU-0.180\ln HM-0.104\ln CLA+0.141\ln PAM$	0.990	0.010
Heihe	$\ln CE=0.113\ln$$GDP+0.050\ln$$IS-0.065\ln$$UR+0.192\ln CC+0.107\ln$$CPR+0.102\ln$$DIU+0.123\ln HM+0.125\ln CLA+0.169\mathrm{n}PAM$	0.914	0.007
Suihua	$\ln CE=0.144\ln$$GDP-0.076\ln$$IS+0.010\ln$$UR+0.170\ln CC+0.091\ln$$CPR+0.121\ln$$DIU+0.144\mathrm{n}HM+0.143\ln CLA+0.124\ln PAM$	0.975	0.025

, it is clear that 12 models were all better fitted. GDP of each city was positively correlated with carbon emissions, indicating that urban development in Heilongjiang Province is costly in terms of carbon emissions, and that economic development has a relatively significant role in promoting carbon emissions in Shuangyashan and Qitaihe. The proportions of secondary production in Harbin, Daqing, Yichun, Jiamusi, and Suihua were inversely related to carbon emissions, and for these cities, industrial restructuring effectively suppressed carbon emissions. The suppression effect of industrial restructuring in several other cities is not yet clear; however, it had a more obvious promoting effect on carbon emissions in Jixi, Qitaihe, and Mudanjiang. The urbanization rates of Harbin, Qiqihar, Jixi, Daqing, Yichun, Jiamusi, Mudanjiang, and Heihe were inversely related to carbon emissions, indicating that urbanization rate plays a suppressive role in carbon emissions. The total population and rural population of each city decreased, the population gathered in the cities, and the increase in the urbanization rate suppressed the carbon emissions of these cities.

Coal consumption in the cities other than Yichun was positively correlated with carbon emissions. Although coal consumption in Yichun decreased, coal consumption there played a suppressive role in carbon emissions. As for other cities, despite the gradual optimization of the urban energy structure, energy consumption currently relies mainly on coal; coal consumption in each city increased annually, and coal consumption promoted urban carbon emissions. Its promoting effects were more evident in Qiqihar, Shuangyashan, and Qitaihe. Except for Jixi, Qitaihe, and Mudanjiang, increases in the urban road area and road mileage promoted urban carbon emissions. Improvements in road facilities promoted development of traffic travel and transportation, which in turn increased transportation carbon emissions. The negative coefficient of per capita disposable income for Jixi and Qitaihe indicated that the higher the disposable income, the lower the carbon emissions, showing that residents gradually develop low-carbon awareness and can actively choose a low-carbon lifestyle. For the other 10 cities, the increase in per capita disposable income did not awaken low-carbon or environmental protection awareness but rather promoted carbon emissions due to increased consumption. The per capita disposable income of Qiqihar played a relatively significant role in promoting carbon emissions. Except for Qiqihar, Jixi, Qitaihe, and Mudanjiang, the increase in construction land area promoted urban carbon emissions; however, the inhibitory effect in Qiqihar was not significant. The coefficient of the total agricultural machinery power was negative in Jixi, which was the only city with a negative coefficient for this factor, indicating that an increase in the total agricultural machinery power input suppressed carbon emissions in Jixi. The other 11 cities had positive coefficients for the total power of agricultural machinery, indicating that the greater the input of the total power of agricultural machinery, the greater the carbon emissions of the cities. The contribution of the total agricultural machinery power to carbon emissions was relatively significant in the cities of Daqing and Qitaihe.

4. Discussion

For Jixi and Qitaihe, their carbon emissions grades decreased. Combined with the results of the STIRPAT model, the urbanization rate, the area of city paved roads at year-end, the per capita disposable income of urban residents, highway mileage, construction land area, and the total power of agricultural machinery had an inhibitory effect on carbon emissions. These influencing factors can be adjusted in combination with urban development to maintain a state of urban carbon emission reduction. For the other 10 cities, whose carbon emissions grade was maintaining or increasing, the industrial structure, urbanization rate, and coal consumption were factors that inhibited urban emissions. Carbon emissions can be reduced by upgrading industrial structures, optimizing population layouts, and reducing coal consumption.

When analyzing the factors influencing carbon emissions, it was concluded that an increase in the urbanization rate suppressed carbon emissions, which differs from the findings of other scholars [70,71]. When most scholars consider more developed regions as research objects, population and urbanization rates show increasing trends and are positively related to carbon emissions. In contrast, the total, urban, and rural populations of Heilongjiang Province show a trend of loss; therefore, the result is contrary to that of other scholars. However, the same conclusion was obtained in other studies in Heilongjiang Province [72]. The energy structure of Heilongjiang Province has gradually been upgraded, and the energy structure is still dominated by coal, accompanied by a yearly increase in carbon emissions. The energy consumption structure of the city, which is overly dependent on coal, has led to a low degree of energy optimization. The inhibitory effect of the decreasing share of coal consumption on carbon emissions is not yet sufficient to balance the negative growth of carbon emissions [73], and the energy consumption structure of Heilongjiang Province has not yet been significantly improved [74]. In the 14th Five-year Plan of Heilongjiang Province, the proportion of industrial output value must increase to 30% by 2025. For most cities, it will be necessary to build a new industrial system and develop strategic new industries to increase the proportion of industry and reach the targets while ensuring green and low-carbon development to help achieve the “dual carbon goals”.

Heilongjiang Province is in a slower development stage than those of the other provinces, and urban construction and economic development should be synergistic with the dual carbon goals, neither one pursued at the expense of the other. Simultaneously, it is important to ensure the living standards of residents and create a livable environment. This study aims to investigate the grade of carbon emission changes and the influencing factors of each city, and to provide a theoretical basis for low-carbon development. Conducted from the perspective of the three living spaces, this study can be better connected with territorial spatial planning and provide reference for planners and policymakers to promote the “dual carbon goals”.

Cities in Heilongjiang Province have gradually introduced new energy vehicles as they develop. Owing to the limitations of the data sources, this study does not distinguish between the carbon emissions of new energy and traditional vehicles, and the carbon emissions of all motor vehicles are still calculated according to the carbon emissions of traditional vehicles. This may cause carbon emissions from traffic travel to decrease or slowly decrease, affecting the analysis of the carbon emission structure and influencing factors. In future research, the carbon emissions from new energy vehicles and traditional vehicles should be calculated separately. The impact of the introduction of new energy vehicles on carbon emissions from transportation, living space, and the total carbon emissions of the PLE spaces in Heilongjiang Province should be further analyzed. Moreover, owing to the limitations of the data sources, this study does not discuss the carbon emissions generated by waste treatment or the carbon emissions of industrial production processes. In future research, we will continue to investigate these areas, supplement our calculation of carbon emissions, and comprehensively analyze the factors influencing carbon emissions in PLE spaces. The obtained 12 STIRPAT models can reflect the impact and mechanism of socioeconomic factors on the carbon emissions of each city. In addition, carbon emissions in the future can be predicted through the model, to judge the time and amount of carbon peak and carbon neutrality. It can be an effective tool for strategic planning and goal formulation of urban carbon control and emission reduction.

5. Conclusions

In this study, we calculated the PLE space carbon emissions in the prefecture-level cities of Heilongjiang Province, analyzed the characteristics of spatial–temporal features and the structure of carbon emissions in segmentation sectors, and analyzed the influence of land use and socioeconomic factors on carbon emissions, reaching the following conclusions:

• Carbon emissions in production and living spaces increased yearly, and Daqing was the city with the highest carbon emissions in production space, followed by Harbin and Qiqihar. Harbin was the city with the highest carbon emissions in living space, followed by Daqing. The carbon sinks of all cities were much smaller than the carbon emissions, and except for a small number of cities that showed an increase in carbon sinks, carbon sinks showed decreasing trends. However, the changes were not significant, and the overall pattern of carbon sink capacity was relatively stable.
• Cities with higher total carbon emissions were concentrated in the southwestern part of Heilongjiang Province, whereas cities in other regions were at a relatively low grade. There was a positive spatial agglomeration phenomenon in 2015 and 2020, and the overall difference in carbon emissions between regions gradually widened.
• Among the six carbon emission segmentation sectors, the proportions of ACE and HCE were smaller, and urban carbon emissions consisted mainly of AHCE, ICE, TCE, and TTCE. The carbon emission structure of each city was also transformed by the adjustment of urban development and industrial structure.
• IPS was the main carbon source space for the other nine cities, including Qiqihar. APS was the main carbon source for Heihe. For Harbin, IPS and URLS were the main carbon sources, and Harbin was the only city in Heilongjiang Province where URLS contributed a large proportion of carbon emissions. For Mudanjiang, there were few differences in the carbon emissions contributed by APS, URLS, and IPS. Even in Daqing, where GES had a larger proportion, FES was the main carbon sink in the city.
• The economy contributed to carbon emissions in all 12 cities. Furthermore, the carbon emissions grade of Jixi and Qitaihe decreased. Urbanization rate, area of city paved roads, per capita disposable income of urban residents, road mileage, construction land area, and total agricultural machinery power inhibited carbon emissions in these cities. For cities with maintaining and increasing carbon emissions grades, industrial structure and coal consumption are factors that could suppress carbon emissions.