Drivers of Carbon Emission in Xinjiang Energy Base: Perspective from the Five-Year Plan Periods

Abstract

Using the Kaya identity and LMDI method, this study analyzes the influence of population, GDP per capita, energy intensity, and carbon intensity on Xinjiang’s carbon emissions, and compares the effects of industrial structure, energy intensity, and carbon intensity on the industrial sectors during the Eighth to Twelfth Five-Year Plan (FYP) periods. Key findings are as follows: (1) Xinjiang’s carbon emissions center on resource- and energy-intensive sectors, emissions from sectors such as extraction of petroleum and natural gas, fuel processing, chemicals, ceramics and cement, iron and steel, and non-ferrous and power generation accounted for 62% of carbon emissions in 2015; (2) after the Sixth FYP, GDP per capita effect turned into the core driver of carbon emission growth, while the population effect played an auxiliary role. Meanwhile, the energy intensity effect exerted a marked inhibitory impact on the increase in carbon emissions, yet the restraining effect of carbon intensity was comparatively limited; (3) during the Eighth to Twelfth FYPs, carbon emission growth was mainly attributed to industrial structure effects of the mining and washing of coal, extraction of petroleum and natural gas, fuel processing, chemicals, ceramics and cement, iron and steel, non-ferrous and power generation. Energy intensity and carbon intensity effects in various industries inhibited emission growth. Based on new trends in Xinjiang’s socioeconomic development, policy recommendations proposed including promoting the low-carbon transformation of industrial structure, profound restructuring of energy consumption, and improving energy efficiency by advancing energy-saving technology.

1. Introduction

It is widely acknowledged that climate change stands among the most urgent challenges facing humanity [1]. Among the contributing factors, carbon emissions stemming from fossil energy consumption are widely recognized as the primary driver of global warming [2]. In recent decades, climate change has emerged as a focal point of discussion among policymakers, industry professionals, and academic researchers [3]. Decomposition studies on carbon emissions and their influencing factors have garnered significant attention, as they offer a scientific framework for assessing the efficacy of related policies. Structural decomposition analysis (SDA), index decomposition analysis (IDA), and production theoretical decomposition analysis (PDA) stand as three widely adopted approaches for carbon emission decomposition [4]. SDA, which was constructed around an input–output model, has experienced substantial growth in applications over recent years. However, its utility is constrained by the reliance on input–output tables, thereby limiting this method primarily to additive decomposition frameworks [5]. One of the distinct merits of PDA analysis lies in its minimal data quality requirements, enabling it to elucidate the driving mechanisms behind carbon emissions, production efficiency, and technological factors. However, a notable limitation of PDA is its inability to depict the structural composition of influencing variables [6]. In contrast to SDA and PDA, IDA emerges as the method of choice, primarily due to its unique advantage of achieving decomposition without residual terms [7]. As the most emblematic method in IDA decomposition, the logarithmic mean division index (LMDI) takes a prominent position [8]. Recently, the LMDI method has been widely applied to studies on carbon emission changes at both regional and sectoral levels [9,10]. A multitude of determinants have been identified as influencing factors. Among them, environmental factors such as emission coefficients [11], carbon emission intensity [12], and the composition of the energy consumption structure play a crucial role [13]. Additionally, economic factors, including energy efficiency [14], production input variables [15], industrial structure [16], and the structure of demand [17], have been shown to impact carbon emissions. From a microeconomic perspective, elements like investment and R&D expenditure have also emerged as significant drivers [18,19]. Technologically, the adoption and advancement of low-carbon innovations, particularly those related to energy utilization, are widely recognized as key factors [20].

Numerous studies adopting the LMDI approach have examined the economic evaluations and driving forces of energy-related carbon emissions in China. Collectively, these studies provide empirical evidence supporting the conclusion that economic activities exert the most significant positive influence on the dynamic changes in energy consumption and carbon dioxide emissions [21]. Enhancing energy efficiency stands as the primary driver in achieving energy conservation and emission reduction goals [22]. While progress in fuel conversion technologies and the scaling up of renewable energy deployments also play crucial roles in mitigating carbon emissions, the impact of industrial structure adjustment remains relatively limited [23]. It is worth noting that the contributions of these influencing factors vary significantly across different sectors. Decomposition analyses at the sectoral level have further illuminated these disparities, revealing sector-specific impacts on the overall emission reduction efforts [24]. China exhibits a significant disparity in economic development and carbon emissions across provinces. Coastal regions have increasingly embraced low-carbon development strategies whereas less-developed inland areas like Xinjiang continue to predominantly depend on energy-intensive industries for economic development. The influence of socioeconomic determinants such as GDP, industrial structure, population, urbanization, and technological level on energy-related carbon emissions varies [25]. These disparities across regions significantly influenced China’s aggregate energy-associated carbon emissions. Functioning as a pivotal national energy base, Xinjiang exhibits a pronounced dependence on resource exploitation. Driven by the rapid expansion of energy-intensive sectors, carbon emissions have exhibited a steep upward trend [26]. Existing studies on carbon emissions in Xinjiang have predominantly focused on identifying the factors that influence them. Various aspects have been explored, including regional changes [27], sectoral carbon emissions and their associated drivers, and population composition [28], as well as policies implemented across different developmental phases [29,30]. These investigations have yielded valuable insights into the factors shaping Xinjiang’s carbon emission patterns.

Policy-making processes in China diverge substantially from those in Western countries, primarily attributed to disparities in political frameworks. China’s developmental strategies typically follow a top-down approach, where the central government formulates national policies informed by prevailing socioeconomic conditions and long-term forecasts. Subsequently, local authorities execute these directives to ensure consistent implementation across regions [31]. China’s Five-Year Plan (FYP) stands as a cornerstone among the nation’s strategic policy instruments. By outlining comprehensive national strategies and objectives, it serves as a linchpin for steering national governance and policy execution. Through successive iterations, the FYP has evolved into a distinctive catalyst propelling China’s economic growth and social progress [32]. The FYP serves as a fundamental framework for steering macroeconomic activities, laying the groundwork for the government’s delivery of public services. Functioning as a pivotal instrument in national governance, it has emerged as a critical benchmark for assessing administrative effectiveness [33]. Accordingly, it is imperative to incorporate the influence of the Five-Year Plans (FYPs) when examining carbon emissions in Xinjiang. The variations observed across different FYP periods can be attributed to the shifting priorities in economic development and the differing degrees of emphasis placed by local governments on carbon emission reduction [32]. Thus, the associations between carbon emissions and economic progress, along with the determinants influencing carbon emissions, are likely to differ across various FYP periods. Meanwhile, among studies investigating the factors affecting carbon emissions in Xinjiang, only a limited number have incorporated policy impacts. Furthermore, analyses of the factors affecting carbon emissions across different FYPs remain scarce. Hence, there is a need for comparative studies on the determinants of carbon emissions in Xinjiang over various FYP stages.

2. Study Area

Situated in northwestern China, Xinjiang is distinguished by a terrain dominated by interlocking basins and mountain ranges [34]. Its inland position far from the ocean, coupled with its role as the hinterland of the Eurasian continent, has made it a vital corridor along the Eurasian Land Bridge since ancient times (Figure 1). As a core hub of the Belt and Road Initiative, Xinjiang remains a developing region in China, currently undergoing accelerated urbanization, agricultural modernization, and industrialization. China’s Energy Development Plan of the 12th Five-Year Plan (2013) explicitly designated Xinjiang for constructing a national comprehensive energy base. Serving as a strategic energy hub of China, Xinjiang not only holds the country’s largest proven reserves of fossil fuels but also abounds in wind and solar energy resources. Specifically, its proven reserves of coal, natural gas, and oil account for 40%, 34%, and 30% of the national totals, respectively [35]. Within China’s national energy distribution networks—including the “North-to-South Coal Transport”, “West-to-East Natural Gas Transmission”, “North-to-South Oil Delivery”, and “West-to-East Power Transmission” projects—Xinjiang assumes a pivotal role in energy export. However, this large-scale energy outward flow has triggered cross-regional carbon emission transfers [36]. While fossil fuel consumption has provided robust support for Xinjiang’s socioeconomic development, the challenges it poses to sustainable development are undeniable. The core dilemma currently facing Xinjiang is how to achieve carbon emission reduction while safeguarding the region’s socioeconomic sustainability.

3. Methods and Data Sources

3.1. Estimation of Carbon Emissions

Carbon emissions from the combustion and utilization of fossil fuels were quantified in accordance with the technical specifications and accounting framework outlined in the 2006 IPCC Guidelines for National Greenhouse Gas Inventories [37], specifically with reference to the standards for selecting emission factors, requirements for statistical data on activity levels, and accounting formulas specified in the Guidelines. This quantitative method not only ensures the scientific and accurate results of carbon emission accounting, but also provides a standardized benchmark for comparing fossil energy carbon emissions. It is also an important technical basis for conducting carbon emission monitoring, evaluation, and formulating emission reduction strategies. The quantification of carbon emissions can formulated as:

$$C_t={\displaystyle \sum _j{E}_t^j}\times Lc{v}_j\times C{f}_t^j\times O_j$$

(1)

In this context, C_t signifies the carbon emissions for year t, while j represents distinct types of fossil fuels. The term $E_t^j$ denotes the consumption of fuel j in year t, and Lcv_j indicates the lower calorific value of fuel j. Additionally, $C{f}_t^j$ refers to the emission factor of fuel j in year t, and O_j stands for the oxidation rate of fuel j.

3.2. Kaya Identity

The Kaya identity fundamentally serves as a quantitative empirical model, enabling the integration of multiple factors—including economic, demographic, energy, technological, and policy dimensions for model simulation [38]. Key indicators for analyzing regional carbon emission patterns typically include population, GDP per capita, energy intensity, and emission coefficients. The Kaya identity can mathematically formulated as:

$$C={\displaystyle \frac{C}{E}}\times {\displaystyle \frac{E}{G}}\times {\displaystyle \frac{G}{P}}\times P$$

(2)

In this context, C, P, G, and E represent carbon emissions, population size, GDP, and energy consumption, respectively. To dissect absolute carbon emission variations and explore sub-sectoral impacts, this research employed the Kaya identity to analyze carbon emission growth [39]. Given that the cumulative carbon emissions from all sectors in year t are denoted as C^t, Equation (2) can further developed as:

$$C^t={\displaystyle \sum _i{C}_i^t}={\displaystyle \sum _i\frac{C_i^t}{E_i^t}}\cdot {\displaystyle \frac{E_i^t}{G_i^t}}\cdot {\displaystyle \frac{G_i^t}{G^t}}\cdot {\displaystyle \frac{G^t}{P^t}}\cdot P^t={\displaystyle \sum _{i}C}{I}_i^t\cdot E{I}_i^t\cdot S_i^t\cdot G^t\cdot P^t$$

(3)

In this framework, $C_i^t$, $E_i^t$, and $G_i^t$ denote the carbon emissions, energy consumption, and GDP of sector i in year t, respectively. P^t signifies the population in year t. $C{I}_i^t$ represents the carbon coefficient of sector i in year t, which is attributed to changes in energy consumption patterns. $E{I}_i^t$ stands for the energy intensity of sector i in year t, $S_i^t$ denotes the economic structure of sector i in year t, and G^t indicates the GDP per capita in year t.

3.3. LMDI Method

In essence, the LMDI approach represents an advanced extension of the Kaya identity. From a practical application perspective, the LMDI method offers several notable advantages. First, it enables complete decomposition without unexplained residual terms, yielding unique and easily interpretable results, which has led to its widespread application in environmental research. Second, the multiplicative LMDI results exhibit an additive property. Third, the additive decomposition of LMDI can quantify the unit contribution of each factor to emissions and establish a clear relationship between additive and multiplicative decomposition. Finally, LMDI maintains consistency during aggregation [7,8]. Numerous studies have explored the influences of drivers at diverse scales on carbon emissions [11,31]. Therefore, the LMDI method was adopted in this paper to examine the impacts of population, GDP per capita, economic structure, energy intensity, and carbon intensity on carbon emissions in Xinjiang. Expressed as follows is the overall variation in carbon emissions from the base year o to the target year t [8,40]:

$$\Delta C=C_t-C_o=\Delta C_{CI}+\Delta C_{EI}+\Delta C_S+\Delta C_G+\Delta C_P$$

(4)

The variation in carbon emissions between base year o and target year t is denoted as ΔC. This ΔC is decomposable into the following parts: carbon intensity effect (designated as ΔC_CI), energy intensity effect (ΔC_EI), economic activity effect (ΔC_S), GDP per capita effect (ΔC_G), and population effect (ΔC_P). Through the application of the LMDI method, the decomposition of Equation (4) can formulated as:

$$\Delta C_{CI}={\displaystyle \sum _i\hspace{1em}}[{\displaystyle \frac{C_i^t-C_i^o}{\ln C_i^t-\ln C_i^o}}\ln ({\displaystyle \frac{C{I}_i^t}{C{I}_i^o}})]$$

(5)

$$\Delta C_{EI}={\displaystyle \sum _i\hspace{1em}}[{\displaystyle \frac{C_i^t-C_i^o}{\ln C_i^t-\ln C_i^o}}\ln ({\displaystyle \frac{E{I}_i^t}{E{I}_i^o}})]$$

(6)

$$\Delta C_s={\displaystyle \sum _i\hspace{1em}}[{\displaystyle \frac{C_i^t-C_i^o}{\ln C_i^t-\ln C_i^o}}\ln ({\displaystyle \frac{S_i^t}{S_i^o}})]$$

(7)

$$\Delta C_G={\displaystyle \frac{C^t-C^o}{\ln C^t-\ln C^o}}\ln {\displaystyle \frac{G^t}{G^o}}$$

(8)

$$\Delta C_P={\displaystyle \frac{C^t-C^o}{\ln C^t-\ln C^o}}\ln {\displaystyle \frac{P^t}{P^o}}$$

(9)

3.4. Data Illustration

Based on the availability of data, the time series data employed in the paper covers the period from 1952 to 2015. Data pertaining to energy, economy, and population was gathered from the Xinjiang Statistical Yearbook. In the context of sectoral carbon emissions, what is considered is the energy consumed across various sectors. Carbon emission verification and comparison data of China and Xinjiang involved in this paper are, respectively, from the Carbon Dioxide Information Analysis Center (CDIAC), the Electronic Data Gathering, Analysis, and Retrieval system (EDGAR), the International Energy Agency (IEA), and the Carbon Emission Accounts and Datasets for emerging economies (CEADS). With the aim of alleviating the impacts of inflation, the time series of GDP figures was modified in accordance with the constant price of 2010, and the source of the Price Index is based on the national level, which is from The China Statistical Yearbook. Standard coal conversion coefficients for various energy sources are derived from GB/T 2589-2008, General Principles for Calculating Comprehensive Energy Consumption [41]. The lower heating values and oxidation rates are excerpted from the IPCC Guidelines for National Greenhouse Gas Inventories. The carbon emission coefficients are sourced from the Guidelines for Compiling Provincial Greenhouse Gas Inventories (

Table 1. Carbon conversion factors for major energy consumption.

Type	Lower Heating Values	Emission Coefficients/ (Mt·PJ−1 CO2)	Oxidation Rates/%
Coal	0.209 08 PJ/104 t	0.087 464	88
Coking coal	0.284 35 PJ/104 t	0.104 292	97
Crude oil	0.418 16 PJ/104 t	0.073 284	98
Gasoline	0.431 24 PJ/104 t	0.069 253	98
Kerosene	0.431 24 PJ/104 t	0.071 818	98
Diesel oil	0.426 52 PJ/104 t	0.074 017	98
Fuel oil	0.418 16 PJ/104 t	0.077 314	98
Natural gas	3.8931 PJ/108 m3	0.056 062	99

). In accordance with the classification standards for national economic industries, and considering both the labor and resource intensity of diverse industries and the traits of Xinjiang’s industrial structure, Xinjiang’s industrial structure is divided into three distinct categories, such as labor-intensive sectors (agriculture, food and tobacco, textile, pulp and paper, ceramics and cement, construction, transportation, trade and catering, service); resource-intensive sectors (mining of metal ores, mining of nonmetal ores, chemicals, iron and steel, non-ferrous, metal and machinery, other manufacturing industries); and energy sectors (mining and washing of coal, extraction of petroleum and natural gas, fuel processing, power generation). To clarify the sector-specific effects, each sector is assigned a unique code in this study (see

Table 2. Code of sectors.

Sectors	Code
Agriculture	A1
Mining and washing of coal	A2
Extraction of petroleum and natural gas	A3
Mining of metal ores	A4
Mining of nonmetal ores	A5
Foods and tobacco	B1
Textile	B2
Pulp and paper	B3
Fuel processing	B4
Chemicals	B5
Ceramics and cement	C1
Iron and steel	C2
Non-ferrous	C3
Metal and machinery	C4
Other manufacturing industries	C5
Power generation	D1
Construction	D2
Transportation	D3
Trade and catering	D4
Service	D5

).

4. Result

4.1. Comparison of Carbon Emission Accounting

With the aim of guaranteeing the accuracy of carbon emissions data in Xinjiang, we estimated the carbon emissions of Xinjiang from 1952 to 2015 and China from 1990 to 2015. Subsequently, we contrasted these data with those from CDIAC, EDGAR, IEA, and CEADs (Figure 2). It was observed that the calculation of China’s carbon emissions between 1990 and 2015 was largely in line with the change trends of CDIAC, EDGAR, CEADs, and IEA, with average errors of −6%, 5%, 1%, and −9% respectively (Figure 2a). In the meantime, though comparing we found that the total Xinjiang carbon emission calculated in this study is basically consistent with the CEADs, with an average error of −2% (Figure 2b). Moreover, these results align with the findings of Shan et al. [42]. It means that the data of carbon emission in Xinjiang are valid and reliable.

4.2. Carbon Emission Trends in Xinjiang During 1952–2015

It is evident that Xinjiang’s carbon emissions exhibited an overall rising tendency based on the changes and growth rates from 1952 to 2015 (Figure 3a). The total carbon emissions rose from 0.87 Mt in 1952 to 468.12 Mt in 2015, increasing 537 times and with an average annual compound growth rate of 12.5%. Specifically, the evolutionary course of Xinjiang’s total carbon emissions can be systematically categorized into three typical phases, with the development tendency of each phase closely linked to China’s national strategic arrangements and regional economic transformation. The first stage (1952–1974) was fluctuating growth in the foundation period. During this period, Xinjiang was in the embryonic phase of national economic system construction, characterized by low social productivity, a traditional fossil energy-oriented energy consumption structure, and carbon emissions remaining at a minimal level. However, as the industrial system was gradually built and with the start of infrastructure construction, the growth rate of carbon emissions showed drastic fluctuation characteristics. The total carbon emissions increased from 0.87 Mt to 14.93 Mt during 1952–1974, with an average annual growth rate of 14.3%, highlighting the pulling effect of resource development on carbon emissions under the planned economic system. The second stage (1975–2000) was a steady rise driven by the Reform and Opening up policy. Following the comprehensive implementation of the Reform and Opening up policy, Xinjiang leveraged the “advantageous resource conversion strategy” to expedite its new industrialization process. During this period, energy-intensive sectors rose rapidly, and projects such as oil and gas resource development and basic industrial construction continued to be implemented, promoting carbon emissions into a steady growth track. With an average annual growth rate of 7.8%, total carbon emissions went up from 23.1 Mt to 93.34 Mt, achieving a three-fold increase and forging a significant positive correlation with the rapid growth of regional GDP. The third stage (2001–2015) was explosive growth under the superposition of strategies. As national strategies like the Western Development and the “Belt and Road” initiative keep moving forward, strong impetus has been injected into the economic development of Xinjiang. The advancement of the energy sectors, the speeding up of infrastructure construction, and the execution of industrial Xinjiang assistance projects have led to the exponential rise in energy consumption. Total carbon emissions surged from 96.24 Mt in 2001 to 468.12 Mt in 2015, demonstrating a 380% increase, with a compound annual growth rate as high as 10.1% (Figure 3a). The growth rate of carbon emissions during this stage was notably higher than in the previous two phases, which verifies the enhancing impact of the resource-dependent economy on carbon emissions in the accelerated period of industrialization.

In light of the tendencies in sectoral carbon emissions resulting from energy consumption in Xinjiang over the period 1990–2015, notable growth was observed in industries including extraction of petroleum and natural gas, fuel processing, power generation, chemicals, non-ferrous, iron and steel, ceramics and cement. Carbon emissions in these sectors experienced remarkable growth over 25 years. Chemicals surged from 3.14 Mt to 95.2 Mt, while the non-ferrous industry showed particularly striking expansion, escalating from 0.67 Mt to 97.96 Mt, with a 145-fold increase with a growth rate far surpassing that of other industries. Fuel processing, power generation, non-ferrous, iron and steel, and the extraction of petroleum and natural gas surged from 2.32 Mt, 1.73 Mt, 3.1 Mt, 3.27 Mt, and 3.06 Mt to 38.39 Mt, 34.71 Mt, 26.29 Mt, 28.25 Mt, and 24.51 Mt, respectively (Figure 3b). Stage-based analysis indicates that after 2010, the chemicals sector entered a phase of rapid expansion, with a 23.3% average annual growth rate—the highest among all industries. This trend is strongly associated with Xinjiang’s strategy to accelerate the clustered and refined development of its chemical sector. By 2015, the above-mentioned industries constituted 62% of Xinjiang’s aggregate carbon emissions (Figure 3b), verifying that resource- and energy-intensive sectors serve as the backbone of regional carbon emissions. This result corresponds to previous and current research concerning Xinjiang [3]. This structural characteristic indicates the profound transition of Xinjiang’s industrial system toward resource-intensive deep processing and heavy chemical industries. Propelled by the sustained execution of the “advantageous resource conversion strategy” and leveraging abundant local oil, gas, and mineral resources, high-energy-intensive and high-emission sectors have expedited their clustering, completely redirecting the carbon emission focus to heavy industrial fields [39]. Thus, the swift expansion of resource and energy-intensive industries not only accentuates Xinjiang’s dependence on resource-deep processing amid industrialization, but also highlights the pressing need to upgrade the energy consumption structure and advance low-carbon industrial transition.

4.3. Decompositional Analysis of Factors Affecting Carbon Emissions Across Different Stages

By means of the refined decomposition of carbon emissions in Xinjiang, the annual effect sizes and contribution degrees of population, GDP per capita, energy intensity, and carbon intensity were accurately obtained. In order to deeply analyzed the impact mechanisms of various factors in specific policy periods, this study took the national FYPs as the timeline for systematic decomposition in different FYP periods. Based on the socioeconomic development characteristics of Xinjiang and the historical context of China’s FYPs, energy consumption-related carbon emissions were systematically categorized into twelve distinct periods, corresponding to the First through Twelfth FYPs (1952–2015). Through in-depth analysis of the factors affecting carbon emissions across different historical periods, this study thoroughly explored the influence intensity and impact magnitude of each key factor on carbon emission fluctuations during distinct policy eras. The relevant research results are shown in the systematic presentation of Figure 4, Figure 5 and Figure 6 and Table 2.

During the First FYP, China focused on promoting industrial construction and developing agricultural production cooperatives, while Xinjiang aligned with the national industrial development strategy of “Steel as the Core”. Although Xinjiang’s economic scale was relatively weak, its growth rate reached 11%, showing a rapid development trend. During this period, the carbon emissions resulting from the energy intensity effect, GDP per capita effect, and population effect reached 0.62 Mt, 0.40 Mt, and 0.26 Mt, respectively, with their contributions to the growth of carbon emissions in Xinjiang accounting for 48%, 32%, and 21% (Figure 4). Carbon intensity effect exhibited an adverse impact, exerting a mild restraining effect on carbon emission growth (

Table 3. Complete decomposition of carbon emission changes under different periods (Mt).

	Population Effect	GDP per Capita Effect	Energy Intensity Effect	Carbon Intensity Effect	Increment
1st FYP	0.26	0.40	0.62	0.01	1.29
2nd FYP	2.68	−0.90	3.79	−0.09	5.48
3rd FYP	1.87	−2.09	3.87	0.88	4.53
4th FYP	2.46	0.54	3.35	4.59	10.94
5th FYP	2.57	9.88	−4.87	−1.92	5.66
6th FYP	1.93	16.96	−8.01	0.89	11.77
7th FYP	5.39	12.47	−3.94	−0.58	13.34
8th FYP	5.35	25.30	−8.22	−1.95	20.48
9th FYP	9.04	25.98	−18.66	2.65	19.01
10th FYP	9.41	56.00	−6.65	−9.40	49.36
11th FYP	15.05	100.27	−37.16	32.24	110.40
12th FYP	29.62	102.64	83.39	−31.84	183.81
1952−2015	101.42	385.07	1.13	−20.37	467.25

). During the Second FYP, including during the “Great Leap Forward Movement”, Xinjiang initiated a massive infrastructure construction drive to achieve ambitious targets for industrial and agricultural production, triggering a surge in energy consumption. However, limited by the underdeveloped technological level of that time, energy utilization efficiency was very low, giving shape to the typical development features of “high energy consumption, high emissions, and low efficiency”. At this stage, the energy intensity effect emerged as the primary factor propelling the growth of carbon emissions. The energy intensity effect and population effect resulted in carbon emissions of 3.79 Mt and 2.68 Mt, and their contributions were 70% and 48%, respectively (Table 3). However, GDP per capita effect and carbon intensity effect exhibited a negative impact, with their contributions amounting to −16% and −2%, respectively (Figure 4). During the Third FYP, the development of China’s national economy was severely disrupted by the Cultural Revolution Movement. The economy of Xinjiang experienced a trajectory from stagnation and downturn to gradual revitalization, with energy consumption-related carbon emissions consequently demonstrating a pattern of “sustained decline—slow growth”. The energy intensity effect remained the core driving force for carbon emission growth, and carbon emissions caused by energy intensity effect, population effect, and carbon intensity effect reached 3.87 Mt, 1.87 Mt, and 0.88 Mt, respectively (Figure 4); it is worth noting that the GDP per capita effect significantly inhibited carbon emission growth with a contribution rate of −46%. During the Fourth FYP (1971–1975), China strengthened the construction of “The Third-Front Movement”, accelerating the process of agricultural mechanization and the infrastructure. During this period, Xinjiang saw a faster growth of its social economy, and the average growth rate of carbon emissions reached 20%. Xinjiang witnessed an acceleration in its social and economic development, and the average annual growth rate of carbon emissions stood at 20%. From the perspective of driving factors, population effect, GDP per capita effect, energy intensity effect, and carbon intensity effect all demonstrated positive driving impacts, with carbon emissions of 2.46 Mt, 0.54 Mt, 3.35 Mt, and 4.59 Mt, respectively (see Table 3).

As shown in Figure 4, there is a significant positive correlation between the energy intensity effect and carbon emissions in Xinjiang from the First FYP to the Fourth FYP, which indicates that technological change has not successfully curbed the growth of carbon emissions. Due to the lagging infrastructure, outdated technology and the impact of unconventional economic activities such as the “Great Leap Forward Movement”, ”Great Steelmaking Movement”, and “Cultural Revolution Movement”, energy utilization efficiency has been remained at a low level for a long time. This made the energy intensity effect the primary factor driving the growth of carbon emissions, with the population effect and GDP per capita effect following [43]. Encouraged by national household registration policies and the “Down to the Countryside” movement, Xinjiang’s population kept increasing at an average annual growth rate of 3.82% during this period [29]. The growing population has placed mounting pressure on resources and the environment, directly propelling the growth of carbon emissions. Amid the combined influence of political instability and population growth, the expansion of economic indicators proceeded at a relatively slow progress, with its pulling effect on carbon emissions being weaker than that of population and energy-related factors [29]. Furthermore, resource endowments constrained the energy consumption structure, so that coal formed a significant proportion, thereby making the carbon intensity effect a key driver of carbon emission growth.

During the Fifth FYP, China strived to establish an independent and relatively complete industrial system and national economic system, and the national economy attained sustained recovery. During this period, Xinjiang’s national economy experienced significant growth, with the GDP per capita effect playing a decisive role in carbon emissions growth, driving an increase of 9.88 Mt in carbon emissions, which accounted for 175% of Xinjiang’s carbon emissions (Figure 5). Notably, the energy intensity effect shifted from a positive driving force to a negative restraining effect in this stage, and the carbon emissions resulting from the energy intensity effect and carbon intensity effect were −4.87 Mt and −1.92 Mt, respectively (Table 3). For the Sixth FYP, following the Third Plenary Session of the 11th Central Committee in 1978, China implemented the “Reform and Opening-Up” policy, shifting its focus to socialist modernization. The Sixth FYP explicitly aimed at addressing the economic bottlenecks left over from the “Cultural Revolution Movement” and strived to promote a fundamental improvement in fiscal and economic conditions. With an average annual GDP growth rate of 12%, Xinjiang’s national economy underwent rapid expansion during this period. Energy utilization efficiency notably enhanced owing to advancements in science and technology. GDP per capita effect continued to be the key driver of carbon emission growth, leading to an increment of 16.96 Mt in carbon emissions, constituting 144% of the total carbon emissions increase in Xinjiang (Figure 5). Both the population effect and carbon intensity effect exhibited positive impacts on carbon emission growth, constituting 16% and 8% of the total emission increase, respectively. Meanwhile, the negative inhibitory effect of energy intensity further enhanced, leading to a reduction of −8.01 Mt of carbon emission increase (Figure 5). In the Seventh FYP, the core tasks of China’s economic development were to foster a conducive environment for economic system reform and facilitate sustainable economic growth. With the in-depth advancement of economic system reform, the social economy achieved steady development. In this stage, Xinjiang’s GDP increased at an average annual rate of 8%. GDP per capita effect and population effect became the primary factors driving carbon emission growth, leading to carbon emissions of 12.47 Mt and 5.39 Mt, which constituted 94% and 40% of the total carbon emission change, respectively. It is noteworthy that the carbon intensity effect transitioned from positive driving to negative inhibition, and the energy intensity effect and carbon intensity effect, respectively, led to a reduction of −3.94 Mt and −0.58 Mt in carbon emissions (see Figure 5). During the Eighth FYP, as the breadth and scale of the “Reform and Opening-Up” policy was sustained and expanded, China established a comprehensive opening framework spanning from coastal to inland regions and from ordinary processing sectors to fundamental industries, while achieving breakthroughs in economic system reform. Meanwhile, Xinjiang established a resource-conversion development strategy, accelerated the layout of emerging industries, and promoted sustained and rapid growth of economy. GDP per capita effect and population effect remained the chief driving factors for carbon emission, contributing 25.30 Mt and 5.35 Mt of carbon emissions, respectively (see Table 3), accounting for 124% and 26% of the carbon emission increment during this stage. Both energy intensity effect and carbon intensity effect were negative, which accounted for −40% and −10% of the increment, respectively.

China’s economic system has undergone a gradual evolution from a planned economy to a market economy since 1978. Xinjiang’s economy has achieved leapfrog development, and GDP increased by 7.1 times from the Fifth FYP to the Eighth FYP. Under the impact of the family planning policy, the average annual population growth rate declined to 1.8%, which made the GDP per capita and population effect the critical factors in carbon emission increment [31]. Serving as a key metric for evaluating economic activities and living standards, GDP per capita-driven economic activities acted as the primary positive factor behind the growth in carbon dioxide emissions. This finding aligns with earlier research conclusions [44]. Notably, energy utilization efficiency and consumption structure underwent concurrent improvements. The proportion of coal consumption kept falling to 66.8%, while economic output per unit of energy consumption increased by 93.1% during the Fifth FYP to the Eighth FYP. While technological advancements have propelled carbon emissions to sustain an upward trend, the restraining impacts of energy intensity and carbon intensity on emissions remained notably weaker than economic scale and population factors, hindered by lagging infrastructure development. Consequently, an effective combined force to reverse carbon emission growth had not yet emerged. This conclusion is highly congruent with the findings of prior research [29,36,39].

In the Ninth FYP, China initially put in place the socialist market economic system, with the framework of “Reform and Opening Up” essentially taking form. In this stage, the GDP per capita impact remained the decisive driver of carbon emission growth, accounting for 136% of the increment in Xinjiang’s carbon emissions (see Figure 6). Population and carbon intensity effect contributed 9.04 Mt and 2.65 Mt of carbon emissions, respectively. It is worth noting that the energy intensity effect saw a marked improvement in comparison with the prior stage, realizing a carbon emission reduction of −18.66 Mt (see Table 3). During the Tenth FYP, following China’s entry into the WTO in 2001, there was a substantial upsurge in investment in energy-intensive industries. This surge propelled a steep rise in energy consumption and a notable resurgence in energy intensity, thereby heralding the onset of a phase characterized by rapid growth in carbon emissions. Meanwhile, as China’s “Western Development” strategy implemented, Xinjiang saw an average annual GDP growth rate of 12.3%. GDP per capita effect persisted as the primary catalyst for carbon emissions, accounting for 55.99 Mt of emissions. In contrast, the energy intensity and carbon intensity effects served to restrain carbon emission growth, with negative contribution rates of −13% and −20%, respectively (as shown in Figure 6). In the Eleventh FYP, as China pressed ahead with the transformation of its economic growth model, Xinjiang increased its efforts in mineral resource development, leading to a continuous upward trend in the proportion of heavy industry’s economic output value within the region’s GDP. Throughout this phase, GDP per capita effect stayed as the primary driver of carbon emission growth, contributing 100.27 Mt of carbon emissions. Meanwhile, the carbon intensity and population effects accounted for 32.24 Mt and 15.05 Mt of emissions (Table 3). Compared with the Tenth FYP, the negative effect of energy intensity has increased, accounting for −34% of Xinjiang’s carbon emission increment. Additionally, in contrast to the Tenth FYP, due to the proportion of coal consumption increased significantly, carbon intensity effect turned positive. In the Twelfth FYP, China’s economy stepped into the “new normal”. As the “Counterpart Aid to Xinjiang” and “The Belt and Road Initiative” strategies have been implemented, the urbanization and industrialization levels in Xinjiang have markedly enhanced. Relying on the superiorities of energy and mineral resources, coupled with the relatively backward production technology, Xinjiang’s energy intensity has increased, and GDP per capita effect and population effect contributed 55% and 16% of emissions, respectively. Conversely, the energy intensity effect transitioned from a negative to a positive influence, and this resulted in 83.39 Mt of carbon emissions, making up 45% of the total increment (Figure 6). Notably, during this stage, the carbon intensity effect shifted from positive to negative, emerging as the sole factor restraining carbon emissions and contributing −16% to the increment in Xinjiang’s carbon emissions.

Since 1990, as the “Reform and Opening-Up” policy has expanded, Xinjiang’s economy has experienced rapid growth [31]. However, due to the continuous growth of energy-intensive industries, economic determinants have emerged as the primary drivers of carbon emissions [3]. Despite the gradual deceleration of Xinjiang’s total population growth rate, it continues to exert a substantial influence on carbon emissions, ranking as the second most influential factor after economic determinants. It is worth noting that the execution of the one-child policy has caused a notable decrease in the population growth rate. Decomposition results show that although the population size effect is a positive driver, its cumulative effect only accounts for 4.3% of the change in total carbon emissions. Population growth is more likely to stimulate fuel consumption by accelerating the process of urbanization or exacerbate pollutant emissions by increasing transportation demand, which is aligned with the current research [25,30,39,45,46]. While the energy intensity and carbon intensity do exert a degree of restraint to the carbon emissions, their overall impact remains relatively modest. The primary rationale behind the minimal contribution of the carbon intensity factor can be attributed to the long-standing entrenchment of an energy consumption structure predominantly reliant on coal. Especially since 2005, a sustained deterioration has occurred in the energy consumption structure, and its impact on carbon emissions has shifted from inhibition to promotion, bringing greater challenges to Xinjiang’s low-carbon transformation. The above findings are consistent with previous studies [29,30,36].

China’s macroeconomic policies have exerted a profound and complex regulatory effect on carbon emissions in Xinjiang. As an economically underdeveloped region, Xinjiang possesses inherent advantages in energy-intensive sectors due to its superior resource endowments. However, its energy consumption remains notably high, primarily attributed to the relative backwardness of local technologies and equipment. In general, China’s Five-Year Plan impacts Xinjiang’s carbon emissions through multiple pathways, including industrial structure adjustment, energy policy optimization, technology innovation-driven development, and coordinated regional development. These policies not only promote local economic growth but also gradually guide Xinjiang toward a low-carbon development trajectory. Against the backdrop of the “Counterpart Aid to Xinjiang” and “The Belt and Road Initiative” strategies, the Five-Year Plans have centered on the development strategy of converting Xinjiang’s resource advantages into economic benefits. While advancing energy industries such as oil and natural gas, emphasis has been placed on the efficient and clean utilization of resources. Typical practices include the construction of large-scale oil and gas fields and integrated refining-chemical projects, where advanced extraction and refining technologies have been adopted to reduce energy loss and carbon emissions. Concurrently, leveraging local advantages in wind and solar energy resources, the development of renewable energy industries has been accelerated to gradually optimize the energy structure and reduce reliance on traditional high-carbon energy sources—thereby exerting a positive mitigation effect on carbon emissions.

4.4. Analysis of Sectors Effect on Carbon Emissions

Based on an industrial analysis, the energy sectors play the most crucial role in driving carbon emissions in Xinjiang (

Table 4. Decomposition of carbon emission by industry in Xinjiang during 1991–2015 (%).

		8th FYP	9th FYP	10th FYP	11th FYP	12th FYP	1991–2015
Economic activity effect	Labor-intensive industries	−0.09	−12.90	−9.91	−2.66	3.21	−1.15
	Energy industries	42.34	197.30	53.79	6.05	−48.91	−3.41
	Resource-intensive industries	1.01	15.88	2.69	16.74	−14.74	5.50
Energy intensity effect	Labor-intensive industries	−5.77	−12.39	1.10	−3.01	−1.17	−2.04
	Energy industries	−43.51	−188.45	−16.24	−14.08	28.26	−15.67
	Resource-intensive industries	−2.54	−39.21	0.86	−12.60	33.90	6.51
Carbon coefficient effect	Labor-intensive industries	1.76	−19.74	−1.73	−1.60	−1.38	−2.44
	Energy industries	−13.52	−13.22	−50.31	−22.38	38.50	13.91
	Resource-intensive industries	0.70	−1.46	2.65	36.58	−8.53	5.47

). During the Eighth to Twelfth FYP periods, the industrial structure effect of the energy sectors showed a positive impact in most stages, accounting for 42.34%, 197.3%, 53.79%, 6.05%, and −48.91% of Xinjiang’s total carbon emissions, respectively (Table 4). However, the energy intensity effect and carbon intensity effect of the energy sectors showed negative impacts in most periods, with the negative effect of energy intensity being particularly prominent, accounting for −43.51%, −188.45%, −16.24%, −14.08%, and 28.26% of Xinjiang’s carbon emissions, respectively (Table 4). The resource-intensive sectors also have a notable impact on carbon emissions in Xinjiang. In most periods, the industrial structure effect of these sectors showed a positive impact, with the degree of influence being relatively slight. Its energy intensity and carbon intensity effects fluctuated significantly, showing alternating positive and negative changes in most stages. Notably, the Twelfth FYP witnessed a remarkable reversal in the industrial structure effect, energy intensity effect, and carbon intensity effect within the energy and resource-intensive sectors. This transformation was largely due to the fact that China’s economic development entered the “new normal” in this period. Despite a slowdown in Xinjiang’s economic growth rate, the total energy consumption persisted in increasing, and the proportion of fossil energy consumption further expanded. Conversely, the structure effects, energy intensity effects, and carbon intensity effects of labor-intensive sectors exerted a minor impact on carbon emissions and displayed negative effects in most periods, suggesting that labor-intensive sectors, to some extent, curbed the growth of carbon emissions in Xinjiang.

From the perspective of industrial structure effects, A3, B4, C2, and D1 have the most significant effects and show positive effects in most periods (Figure 7). As illustrated in Figure 8, energy intensity is more significantly influenced by A2, A3, B4, C2, and D1, with negative effects being the dominant trend throughout most periods. In contrast to other sectors, A2, A3, B2, B4, B5, C3, and D1 demonstrate relatively stronger carbon intensity effects. Nevertheless, the influence of these industries on Xinjiang’s carbon emissions is inconsistent, showing significant fluctuations, with the effects shifting repeatedly between positive and negative values (as shown in Figure 9).

5. Discussion

The Five-Year Plan is one of the most important national policy tools in China [32]. It provides clear national strategies and intentions, which can not only effectively encourage the government to govern proactively but also fully arouse market vitality, enabling the market to play a decisive role in resource allocation and, thus, boosting more efficient government operations. Driven by the support of macro mechanisms, China has forged a distinct model and path for energy conservation and emission reduction during the implementation of the FYP. Without this crucial macro-level backing, the advancement of such initiatives would be severely constrained [32]. From the period of the First to the Fifth FYPs, planning became the core mode of resource allocation, and the function of market regulation was extremely limited. Under the planning instructions, Xinjiang carried out some preliminary industrial construction, such as traditional energy mining. However, due to backward mining technologies and equipment, as well as the lack of scientific planning and advanced technical support, energy utilization efficiency was very low. A large amount of coal and other energy was consumed in a rough way, resulting in a substantial increase in carbon emissions. During this historical stage, a series of political movements, such as the “Great Steelmaking”, “Cultural Revolution”, and “Down to the Countryside” movements, seriously disrupted the order of economic development, resulting in slow economic development and irrational industrial structure in Xinjiang [29]. Due to the relatively weak infrastructure and backward production technology in Xinjiang, its energy utilization efficiency was far lower than the national average. The energy intensity effect significantly promoted carbon emission increases in this period, and this conclusion is consistent with the research results of many scholars [29,30,36].

Since 1978, China has made comprehensive adjustments to the orientation of national economic development, gradually shifting from a planned economy to a market-oriented economy. As the market’s role in resource allocation became more prominent and the FYPs placed increasing stress on the quality and efficiency of economic development, the economic efficiency of Xinjiang gradually broke free from the limitations imposed by the economic system constraints during the Cultural Revolution and witnessed notable improvement [47]. During the Eighth FYP, Xinjiang established a strategy for the transformation and development of advantageous resources, and vigorously promoted this strategy by leveraging its abundant mineral resources to accelerate the development process of new industries. At this stage, motivated by the national campaign to enhance technological levels in industrial enterprises, Xinjiang’s traditional energy-intensive industries underwent equipment upgrades and process improvements to some extent. As a result, energy utilization efficiency increased, and the carbon emission intensity began to decline [30]. Emissions during this time mainly originated from industries such as mining and washing of coal, extraction of petroleum and natural gas, fuel processing, chemicals, ceramics and cement, iron and steel, and power generation. Moreover, the expansion of carbon emissions mainly stemmed from the influence of the industrial structure effect. During this period, the growth rate of energy consumption in Xinjiang was significantly lower than that of economic growth, and carbon emissions were decreased due to the effects of energy intensity and carbon intensity in most industries. Nonetheless, because coal consumption accounts for a large portion of power generation, the carbon intensity impact of the power generation sector kept rising, accumulating 19.83 Mt of carbon emissions throughout the Eighth and Ninth FYP periods. At the same time, the industrial structure was undergoing gradual adjustment, and low-energy industries such as the services achieved certain development, further optimizing the carbon emission structure.

Since China acceded to the WTO in 2001, there has been a sustained rise in investment directed towards energy-intensive industrial manufacturing. During the Tenth FYP period, driven by the “Western Development” strategy, and with Xinjiang established as a national energy strategic base during the Eleventh FYP period, the economic growth of Xinjiang’s industrial sector achieved significant growth. Relying on Xinjiang’s rich energy resources, energy-intensive industries have developed rapidly. As a result, energy consumption has increased substantially, energy intensity has rebounded, and carbon emissions have grown quickly. Since 2001, Xinjiang’s key carbon-emitting sectors have mainly clustered in domains including extraction of petroleum and natural gas, fuel processing, chemicals, ceramics and cement, iron and steel, and power generation. This finding is in substantial agreement with the research outcomes of many scholars [29,30,36]. Since the Eleventh FYP, China has put forward policies for industrial structure optimization and economic structure adjustment. These policies intended to transform the driving force of economic growth from industrialization to the service industry, and have successfully promoted the upgrading and optimization of the industrial structure [32]. During this period, Xinjiang took an active approach to national policies, enhanced supervision over energy conservation and emission reduction, reinforced environmental protection, strongly promoted the development of circular economy, and set up an evaluation system as well as a system for energy conservation, consumption reduction, and emission reduction [48]. At the same time, advanced energy-saving technologies and equipment adopted in major emission industries such as extraction of petroleum and natural gas, fuel processing, and efficient oil and gas extraction equipment and energy-saving coke ovens have been promoted. There has been a significant decrease in energy consumption in industries such as iron and steel, ceramics and cement, and mining and washing of coal, achieved through endeavors to phase out obsolete production capacity or conduct technological upgrades. During the Twelfth FYP, China’s economy entered the “new normal”. Xinjiang experienced a slowdown in economic growth. In the meantime, its total energy consumption persisted in increasing. Benefiting from resource endowment advantages, energy-intensive industries saw a higher share of fossil fuel consumption. This led to reversals in the industrial structure effect, energy intensity effect, and carbon intensity effect of these sectors. Moreover, how the economic growth effect interacted with the emission reduction effect of environmental protection policies is illustrated by the changing carbon emission intensity [49]. During this period, adjustments were made to the industrial structure of Xinjiang. Low-energy-consuming sectors such as the services achieved some development, and tourism gradually rose, which reduced the dependence of Xinjiang’s economy on high-energy-consuming sectors. Meanwhile, Xinjiang intensified its efforts to develop and utilize clean energy sources such as wind power and solar energy. This measure lowered the reliance on traditional fossil fuels and, thus, reduced carbon emissions.

The impact of China’s Five-Year Plans on carbon emissions in Xinjiang can be interpreted based on the multi-scale energy transition theory. This theory posits that the interaction between policy-driven institutional changes, resource endowment constraints, and technological diffusion collectively shapes regional decarbonization pathways—and FYPs serve as the “policy anchor” for Xinjiang’s energy development. For instance, during the Eleventh to Twelfth FYP periods, policies focused on coal-based industrialization to leverage Xinjiang’s abundant coal resources. This aligns with the “resource endowment-driven transition” phase in the theory, which also explains the surge in carbon emissions during the same period. It demonstrates that by adjusting Xinjiang’s position in the energy transition process, FYPs effectively connect national goals with regional comparative advantages. The above findings are further validated by comparing with international experiences from regions with similar geographical characteristics and resource endowments. Western Australia, for example, is also a remote, resource-rich region that has long relied on coal and mineral processing to drive economic growth. Its Sustainable Energy Transformation Strategy (2020–2030)—analogous to China’s FYPs—achieved a 12% reduction in industrial carbon intensity within five years through renewable energy subsidies and stricter coal-fired power emission standards [50]. Kazakhstan, facing similar challenges, implemented its Green Economy Transition Program (2019–2030). By introducing “carbon intensity caps” for the oil and gas sector and investing in cross-border green power grids, Kazakhstan reduced emissions from energy-intensive industries by 8% while maintaining stable industrial output [51]. In conclusion, the trajectory of carbon emissions in Xinjiang conforms to the general laws of regional energy transition. Policy flexibility (adapting to resource endowments) and cross-sector coordination are key to balancing economic development and decarbonization. This further highlights the unique role of China’s FYP system in accelerating regional energy transitions compared to market-driven models.

6. Conclusions and Policy Recommendations

6.1. Conclusions

The main research conclusions are as follows: (1) Xinjiang’s carbon emissions surged from 0.87 Mt in 1952 to 468 Mt in 2015, and sectors such as extraction of petroleum and natural gas, fuel processing, chemicals, ceramics and cement, iron and steel, non-ferrous, and power generation collectively contributed to 62% of Xinjiang’s carbon emission in 2015. (2) GDP per capita effect became the core factor that drives carbon emission growth after the Sixth FYP period. Concurrently, the population effect continues to act as a contributing factor for such incremental growth. In sharp contrast, the energy intensity effect has established itself as a key mitigating force against the rise in carbon emissions. Although carbon intensity also plays a modest role in curbing emissions, its overall impact remains relatively restricted. (3) From the Eighth FYP to Twelfth FYP periods, the carbon emissions increase was mainly attributed to the industrial structure effects of resource processing sectors such as mining and washing of coal, extraction of petroleum and natural gas, fuel processing, chemicals, ceramics and cement, iron and steel, non-ferrous, and power generation. In contrast, the energy intensity effects and carbon intensity effects of various sectors had an effect on curbing the increase in carbon emissions.

6.2. Policy Recommendations

In light of the new trends in Xinjiang’s socioeconomic development, this paper puts forward a set of policy recommendations for low-carbon development, with the goal of optimizing the structure of the industrial sector and comprehensively promoting the realization of energy efficiency and emission reduction targets.

(1)

The low-carbon transformation pathway of industrial structures.

Local governments should formulate industrial upgrading policies tailored to regional characteristics and adopt differentiated strategies based on the varying carbon reduction effects of structural adjustments across industrial sectors. It is imperative to break the habitual dependence on energy-intensive industries, strictly supervise the expansion of new production capacity in energy-intensive sectors, and promote the upgrading and transformation of existing production capabilities. For high-emission industries—including electricity generation, ferrous metal smelting, coal mining, non-ferrous metal processing, and fuel refining—dynamic production capacity replacement and total energy consumption control should be implemented. Projects with energy efficiency below the benchmark level should be prioritized for phased elimination to guide industries towards the development of in-depth resource processing. Meanwhile, leveraging Xinjiang’s position as a core region of the Belt and Road Initiative, local authorities should actively undertake the transfer of low-energy-consuming industries (e.g., new energy equipment manufacturing and electronic information industries) from eastern China and foster clusters of strategic emerging industries. Additionally, it is essential to strictly curb the unregulated expansion of coal production capacity, focus on developing modern coal chemical industries (such as coal-to-oil and coal-to-natural gas), and enhance the comprehensive utilization efficiency and added value of coal resources.

(2)

Profound Restructuring of Energy Consumption.

It is imperative to break the habitual dependence on energy-intensive industries, strictly supervise the development of new production capacity in energy-intensive sectors, and promote the upgrading and transformation of existing production capabilities. Currently, fossil fuels remain the dominant component of energy consumption in Xinjiang, with coal as the primary source. Empirical evidence has shown that the adoption of clean energy and the restructuring of the energy mix can significantly reduce the share of energy sources with high-carbon-emission coefficients. Consequently, optimizing the energy structure has emerged as a pivotal focus for future carbon reduction efforts. On the one hand, it is necessary to accelerate the substitution of clean energy and develop a new-type power system. This involves continuously increasing the proportion of renewable energy in total energy consumption while promoting the flexible transformation of coal-fired power plants. By enhancing the peak-shaving capacity of thermal power units, the intermittent fluctuations of renewable energy can mitigated. Meanwhile, the approval of new coal-fired power projects should strictly controlled to avoid the risk of “carbon lock-in”. Furthermore, it is essential to vigorously advance the construction of wind and solar power plants while strengthening the infrastructure of smart grids. When formulating grid-connection policies, priority should be given to ensuring unobstructed consumption channels for non-fossil energy sources, with the aim of improving the overall capacity for clean energy consumption and distribution. In addition, the implementation of electrification substitution strategies for terminal energy consumption should be accelerated. Special action plans for “coal-to-electricity” in the industrial sector should be developed, and advanced technologies such as electric arc furnace steelmaking should be promoted in energy-intensive industries (e.g., steel and electrolytic aluminum). This approach will facilitate the achievement of carbon reduction targets from the terminal energy consumption link.

(3)

Technological pathways for improving energy efficiency

To further advance the industrial energy efficiency revolution, it is imperative to mandate the implementation of the contract energy management (CEM) model in key energy-consuming enterprises. Meanwhile, efforts to promote energy-saving technologies—such as waste heat and pressure recovery, and smart grid optimization—should be vigorously scaled up. Continuous progress should also be made in driving technological upgrading and low-carbon transformation in the power sector. For industries including chemicals and non-ferrous metal processing, the development of emission standards and the implementation of full-process pollution prevention and control measures need to be strengthened, thereby reducing carbon emission intensity at the source. In parallel, the deep integration of the coal chemical industry with renewable energy sources should be actively promoted. This includes accelerating the research and development (R&D) of zero-carbon coal chemical technologies, expanding industrial chains, and increasing industrial added value. Additionally, priority should be given to addressing key technical bottlenecks through collaborative R&D initiatives. A dedicated fund for carbon neutrality technology R&D in Xinjiang should be established, and an integrated “production-university-research-application” innovation platform should be built—leveraging research institutions such as the Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences (CAS). These measures will accelerate the conversion of scientific and technological achievements into practical productivity, thereby providing technical support for improving energy efficiency.