The Spatiotemporal Decoupling Relationship between Economic Development, Energy Consumption, and Carbon Dioxide Emissions in Xinjiang Province from 2006 to 2020

Abstract

Understanding the spatiotemporal decoupling effects among economic development, energy consumption, and carbon dioxide emissions is paramount to achieving sustainable development. This relationship sheds light on how regions can grow economically while managing their energy resources efficiently and minimizing environmental impacts. This study examines the critical and globally relevant issue of spatiotemporal decoupling that includes economic development, energy consumption, and carbon dioxide emissions in Xinjiang Province from 2006 to 2020. The Tapio Elasticity Analysis Method is utilized to achieve this objective. We found that the early years showed expansive coupling, reflecting a phase where economic growth was closely tied to increases in energy consumption and emissions. However, over time, particularly post-2010, there is a noticeable shift towards weak decoupling and eventually to more substantial forms of decoupling. The primary sector displayed mostly weak and strong decoupling. The secondary sector, however, showed fluctuating decoupling states. In the tertiary sector, a generally weak decoupling was observed. A spatial analysis across Xinjiang’s prefectures and cities revealed pronounced regional variations. This investigation validates the effectiveness of regional ecological policies and illustrates the necessity of tailored strategies to foster sustainable development. Our findings provide valuable insights not only for regional policymakers but also for international stakeholders aiming to achieve sustainable development. The results underline the importance of tailored strategies in different regions, contributing to the broader understanding of sustainable development dynamics.

1. Introduction

The interaction between energy consumption, environmental protection, and economic growth has been increasingly apparent in recent decades, and these concerns are now of paramount worldwide importance [1,2,3]. This nexus underscores the intricate relationship between economic development, the utilization of energy resources, and the imperative to safeguard the environment. As economies pursue growth and industrialization, their dependence on energy resources inevitably increases, leading to higher energy consumption and subsequent environmental degradation [4,5,6]. Economic growth serves as the cornerstone of modern societies, driving progress and prosperity. Over the past decades, the global economy has witnessed substantial expansion, spurred by technological advancements, globalization, and policy reforms. This economic growth has lifted millions out of poverty and facilitated improvements in living standards [7,8]. However, this progress comes with a significant rise in energy consumption, presenting challenges to environmental sustainability [9,10].

Energy consumption is intrinsically linked to economic activities, serving as a fundamental driver of industrial production, transportation, and residential needs. As economies expand, their demand for energy escalates, leading to heightened exploitation of finite resources such as fossil fuels. The combustion of these resources releases greenhouse gases, exacerbating climate change and posing grave environmental threats [11,12]. Consequently, the necessity of implementing sustainable energy practices is highlighted by the correlation between economic growth and energy consumption [13,14,15,16]. Amidst burgeoning economic growth and escalating energy consumption, the imperative for environmental protection has assumed paramount importance. Environmental degradation, manifesting in air and water pollution, deforestation, and climate change, poses grave threats to ecosystems and human health. Recognizing the urgency of this issue, nations worldwide have pledged to adopt measures to mitigate environmental degradation and transition towards sustainable development pathways [17,18]. In conclusion, the intricate interweaving of economic growth, energy consumption, and environmental protection is a salient feature of contemporary global challenges. As nations navigate the path towards sustainable development, addressing this nexus is imperative. By adopting holistic approaches that balance economic growth with energy efficiency and environmental stewardship, societies can foster resilient and sustainable development trajectories for future generations [19].

China is experiencing rapid economic development, driven by extensive industrialization and urbanization, resulting in substantial increases in energy consumption [20,21,22,23,24]. This heightened consumption has led to increased environmental pressures, including air and water pollution, biodiversity loss, and climate change impacts. Consequently, China has recognized the imperative to balance economic expansion with environmental protection through its ecological civilization construction initiatives [25,26]. These efforts prioritize sustainable development, resource conservation, and environmental stewardship, aiming to achieve harmony between economic prosperity and ecological sustainability [27,28]. As China strives to transition towards a greener and more sustainable development model, the interdependence of economic growth, energy consumption, and environmental protection has become increasingly apparent. Therefore, concerted efforts are underway to enhance energy efficiency, promote renewable energy sources, and implement stringent environmental regulations to mitigate environmental degradation and foster ecological resilience. By integrating ecological considerations into its development strategies, China endeavors to chart a path towards a more balanced and sustainable future, exemplifying a global commitment to harmonizing economic progress with environmental preservation [29,30].

As a pivotal province in China’s western region, Xinjiang boasts abundant resource reserves and unique geographical advantages, serving as a vital engine of China’s economic growth for decades [31,32,33]. However, alongside its rapid economic development, Xinjiang also confronts formidable challenges in energy consumption and environmental protection [32]. These challenges stem from the region’s increasing industrialization, urbanization, and infrastructure development, which have escalated energy demands and environmental pressures [34,35]. Consequently, understanding the intricate relationship between Xinjiang’s economic growth and energy consumption, as well as their implications for carbon dioxide emissions, has become imperative. By examining these dynamics, policymakers can devise targeted strategies to promote sustainable development, enhance energy efficiency, and mitigate environmental degradation in Xinjiang. Moreover, given Xinjiang’s strategic importance in China’s Belt and Road Initiative and its significant contributions to national economic growth, addressing these challenges is not only crucial for regional development but also for advancing China’s broader goals of ecological civilization construction and global environmental stewardship [32,36]. Hence, conducting a thorough analysis of Xinjiang’s economic dynamics and its environmental consequences can offer valuable perspectives for policymakers, stakeholders, and researchers aiming to understand the complex connection between growth in the economy, consumption of energy, and protecting the environment in Xinjiang and other areas.

Building upon this foundation, the present study aims to investigate the temporal and spatial decoupling relationship between economic development, energy consumption, and carbon dioxide emissions in Xinjiang from 2006 to 2020. The specific research objectives include (1) analyzing the decoupling relationship among economic growth (denotes by GDP), energy consumption, and carbon dioxide emissions in Xinjiang during the specified period; (2) examining the decoupling relationship among the development (denotes by GDP) of the primary, secondary, and tertiary industries, energy consumption, and carbon dioxide emissions in Xinjiang from 2006 to 2020, with particular emphasis on the relationship between the development (denotes by GDP) of the secondary industry and energy consumption; and (3) exploring the decoupling relationship between economic growth (denotes by GDP) and energy consumption across different cities and prefectures in Xinjiang during the same timeframe. Through the examination of these research objectives, this study seeks to validate the following hypotheses: (1) during the study period, with the implementation of ecological civilization construction, Xinjiang’s economic growth demonstrates a favorable decoupling status from energy consumption; (2) there are variations in the decoupling relationship between economic growth and energy consumption across different sectors of the economy; and (3) spatially, significant differences exist in the decoupling relationship between economic growth and energy consumption across different regions of Xinjiang. This study intends to obtain a comprehensive understanding of the intricate interactions between economic growth, energy consumption, and environmental sustainability in Xinjiang by performing a thorough investigation of the research objectives and hypotheses. The insights gained from this study can be applied to other regions globally, especially those experiencing rapid industrialization and urbanization. For instance, similar decoupling strategies can be considered in other developing regions to balance economic growth with environmental sustainability.

2. Methods

2.1. Study Area

Xinjiang Province, situated in the northwest of China (Figure 1), spans a vast area characterized by diverse geographical landscapes and climates. With coordinates ranging from approximately 35° N to 48° N latitude and 73° E to 96° E longitude, Xinjiang encompasses an expansive territory that borders eight countries, including Mongolia, Russia, Kazakhstan, Kyrgyzstan, Tajikistan, Afghanistan, Pakistan, and India. This strategic location positions Xinjiang as a key crossroads connecting East Asia, Central Asia, and Europe, facilitating trade, cultural exchange, and economic cooperation [37]. The region’s climatic conditions vary significantly from north to south and from east to west, influenced by its diverse topography, ranging from high mountain ranges and plateaus to vast deserts and grasslands. Generally, Xinjiang experiences a continental climate, characterized by hot summers and cold winters, with temperature variations influenced by elevation and distance from the coast [37]. As one of China’s key provinces in the western region, Xinjiang plays a crucial role in driving the nation’s economic growth and development. Its rich resource reserves, including oil, natural gas, coal, and various minerals, contribute significantly to China’s energy security and industrial output [38]. Additionally, Xinjiang’s strategic location along the Belt and Road Initiative provides it with immense potential for economic development and international trade. Overall, Xinjiang’s unique ecological and environmental characteristics, coupled with its complex socioeconomic and environmental dynamics, makes it a compelling choice for conducting research on the decoupling relationship between economic growth, energy consumption, and carbon dioxide emissions.

2.2. Data Sources

The primary data source utilized in this study is the Xinjiang Uyghur Autonomous Region Statistical Yearbook for the years 2007 to 2021. The selected data encompass a range of key indicators, including regional gross domestic product and fossil fuel consumption, as well as the GDP of the primary, secondary, and tertiary industries. Additionally, the total energy consumption for each prefecture in Xinjiang (measured in metric tons of standard coal) is included in the analysis. Furthermore, the energy consumption data are disaggregated by industry, comprising the energy consumption of the primary industry (encompassing agriculture, forestry, animal husbandry, and fishery), the secondary industry (Mining and Washing of Coal; Extraction of Petroleum and Natural Gas; Mining and Processing of Ferrous Metal Ores; Mining and Processing of Nonferrous Metal Ores; Mining and Processing of Nonmetal Ores; Processing of Food from Agricultural Products; Manufacture of Food; Manufacture of Wine, Beverages and Refined Tea; Manufacture of Tobacco; Manufacture of Textile; Manufacture of Textile Wearing Apparel, Footwear and Caps; Leather, Fur, Feather and Related Product Manufacturing; Processing of Timber, Wood, Bamboo, Cane, and Grass Products; Manufacture of Furniture; Manufacture of Paper and Paper Products; Printing and Copying of Media for Record; Oil Processing, Coking, and Nuclear Fuel Processing; Raw Chemical Material and Chemical Products; Manufacture of Medicine; Manufacture of Chemical Fiber; Manufacture of Rubber Products; Manufacture of Nonmetal Mineral Products; Smelting and Pressing of Ferrous Metals; Smelting and Pressing of Nonferrous Metals; Manufacture of Metal Products; Manufacture of General-Purpose Machinery; Manufacture of Special-Purpose Machinery; Manufacture of Electric Equipment and Machinery; Electricity and Thermal Production and Supply, Gas Production and Supply; Water Production and Supply), and the tertiary industry (encompassing transportation, postal, telecommunications, commerce, catering, material supply and marketing, warehousing, and household consumption). Given the pivotal role of the industrial sector in Xinjiang’s socioeconomic development, data on the economic production and energy consumption of all industrial sectors within the secondary industry are also collected. This comprehensive dataset enables a detailed examination of the interplay between economic growth, sectoral development, and energy consumption patterns in Xinjiang over the specified period.

2.3. The Decoupling Model

Decoupling analysis refers to the process of separating the relationship between economic growth and resource consumption/environmental impact [39]. Typically, there exists a positive correlation between economic development, energy consumption, and environmental pollution, where economic growth drives increased energy consumption, leading to higher emissions of greenhouse gases like carbon dioxide, exacerbating environmental issues. In the present study, a decoupling analysis was used to explore the degree to which economic growth can be achieved without increasing resource consumption and environmental pollution, or whether the growth rate of resource consumption and environmental pollution can be lower than that of economic growth, thereby achieving the decoupling of economic growth from resources and the environment.

Currently, the predominant models utilized for decoupling analyses include the OECD (Organization for Economic Co-operation and Development) Decoupling Index Method and the Tapio Elasticity Analysis Method [40,41,42]. These two decoupling models employ different calculation methodologies, which may yield differing results. The OECD Decoupling Index Method is limited in its ability to only determine whether decoupling has occurred or not, without providing explicit quantification of the degree or category of decoupling [43,44]. Moreover, its results are susceptible to the influence of the selected base year data, thereby constraining its applicability. Conversely, the Tapio Elasticity Analysis Method addresses the limitations of the OECD Decoupling Index Method. It enhances the accuracy of decoupling degree and category determination, offering a more precise reflection of the relationship between energy consumption and economic development in a given region [45]. By overcoming the drawbacks of the OECD method, the Tapio Elasticity Analysis Method provides a more nuanced understanding of decoupling dynamics, facilitating more informed policy interventions and sustainable development strategies.

This study adopts the Tapio Elasticity Analysis Method to establish the decoupling model as follows:

$${\mathrm{n}}_{\mathrm{E}\mathrm{G}}={\displaystyle \frac{∆\mathrm{E}}{∆\mathrm{G}}}={\displaystyle \frac{\left({\mathrm{E}}_{\mathrm{t}}-{\mathrm{E}}_0\right)/{\mathrm{E}}_0}{\left({\mathrm{G}}_{\mathrm{t}}-{\mathrm{G}}_0\right)/{\mathrm{G}}_0}}$$

(1)

$${\mathrm{n}}_{\mathrm{C}\mathrm{G}}={\displaystyle \frac{∆\mathrm{C}}{∆\mathrm{G}}}={\displaystyle \frac{\left({\mathrm{C}}_{\mathrm{t}}-{\mathrm{C}}_0\right)/{\mathrm{C}}_0}{\left({\mathrm{G}}_{\mathrm{t}}-{\mathrm{G}}_0\right)/{\mathrm{G}}_0}}$$

(2)

$${\mathrm{n}}_{\mathrm{E}\mathrm{C}}={\displaystyle \frac{∆\mathrm{E}}{∆\mathrm{C}}}={\displaystyle \frac{\left({\mathrm{E}}_{\mathrm{t}}-{\mathrm{E}}_0\right)/{\mathrm{E}}_0}{\left({\mathrm{C}}_{\mathrm{t}}-{\mathrm{C}}_0\right)/{\mathrm{C}}_0}}$$

(3)

where n refers to the decoupling index, which is a metric used to analyze the relationship between economic growth and environmental pressure. ∆E, ∆G, and ∆C represent yearly rate of change in energy consumption, GDP, and carbon emissions, respectively; that is, $∆\mathrm{E}$ = ${\mathrm{E}}_{\mathrm{t}}-{\mathrm{E}}_{\mathrm{t}-1},∆\mathrm{G}$ = ${\mathrm{G}}_{\mathrm{t}}-\mathrm{G},∆\mathrm{C}$ = ${\mathrm{C}}_{\mathrm{t}}-{\mathrm{C}}_{\mathrm{t}-1}$.

The decoupling status can be classified into three major categories (decoupling, negative decoupling, and coupling) and eight subcategories according to

Table 1. Tapio decoupling categories.

Decoupling Status		ΔE/ΔC	ΔG	Decoupling Index	Significance
Decoupling	Weak decoupling	>0	>0	$0 < {\mathrm{n}}_{\mathrm{E}\mathrm{G}}$ < 0.8	Energy consumption growth rate below economic growth rate
	Strong decoupling	≤0	>0	${\mathrm{n}}_{\mathrm{E}\mathrm{G}}$ < 0	Energy consumption decreases, but economic growth increases
	Recessive decoupling	<0	<0	${\mathrm{n}}_{\mathrm{E}\mathrm{G}}$ > 1.2	Economic recession rate lower than the rate of energy consumption decline
Negative Decoupling	Weak negative decoupling	<0	<0	0 < ${\mathrm{n}}_{\mathrm{E}\mathrm{G}}$ < 0.8	Energy consumption decline rate lower than economic recession rate
	Strong negative decoupling	≥0	<0	${\mathrm{n}}_{\mathrm{E}\mathrm{G}}$ < 0	Economic recession occurs, but the rate of energy consumption increases
	Expansive negative decoupling	>0	>0	${\mathrm{n}}_{\mathrm{E}\mathrm{G}}$ > 1.2	The growth of the economy has not kept pace with the rate of energy consumption.
Coupling	Expansive coupling	>0	>0	0.8 < ${\mathrm{n}}_{\mathrm{E}\mathrm{G}}$ < 1.2	The growth rate of the economy is essentially equal to the growth rate of energy consumption
	Decay coupling	<0	<0	0.8 < ${\mathrm{n}}_{\mathrm{E}\mathrm{G}}$ < 1.2	The rate of economic recession is essentially equal to the rate of decline in energy consumption

, due to the differences in n, ∆E, and ∆G.

The energy consumption of Xinjiang relies primarily on coal, oil, and natural gas, with limited utilization of clean energy sources including hydro, wind, and solar power. Consequently, carbon emissions calculations exclude clean energy sources. Moreover, there is no universally accepted standard for carbon emission coefficients for different energy sources. Therefore, after a comparative analysis of various references, the mean value was chosen for these coefficients (

Table 2. Carbon emission coefficients by fuel (t C t − 1).

Source	Coal	Gasoline	Natural Gas
DOE/EIA [46]	0.70	0.48	0.39
Institute of Energy Economics, Japan [47]	0.76	0.59	0.45
China Institute of Energy economics research [48]	0.73	0.58	0.41
Xu et al. [49]	0.75	0.58	0.44
Mean	0.73	0.56	0.42

). The corresponding formula is as follows:

$${\mathrm{C}}_{\mathrm{t}}=\sum {\mathrm{E}}_{\mathrm{t}\mathrm{i}}{\mathsf{\gamma }}_{\mathrm{i}}$$

(4)

where ${\mathrm{C}}_{\mathrm{t}}$ represents the total carbon emissions in year t; ${\mathrm{E}}_{\mathrm{t}\mathrm{i}}$ and ${\mathsf{\gamma }}_{\mathrm{i}}$ represent the consumption of the i-th type of energy in year t and the carbon emission coefficient of the corresponding energy.

3. Results

3.1. The Decoupling Relationships among GDP, Carbon Dioxide Emissions, and Energy Consumption

Between 2006 and 2020, there was a discernible upward trajectory in GDP, energy consumption, and carbon dioxide (CO₂) emissions in Xinjiang. Nevertheless, the rates of change exhibited considerable variability. The rate of economic growth accelerated, while the growth rates of energy consumption and carbon dioxide emissions gradually declined. During the duration of the study period, there was a discernible shift in the decoupling patterns observed in the consumption of energy, the growth of GDP, and the release of carbon dioxide emissions in the region of Xinjiang. The observed changes can be classified into three distinct categories, which are designated as “weak decoupling”, “expansive negative decoupling”, and “expansive coupling”. These findings are presented in

Table 3. Decoupling status of economic growth, energy consumption, and carbon dioxide emissions in Xinjiang.

Period	${\mathbf{n}}_{\mathbf{E}\mathbf{G}}$	Decoupling State	${\mathbf{n}}_{\mathbf{C}\mathbf{G}}$	Decoupling State	${\mathbf{n}}_{\mathbf{E}\mathbf{C}}$	Decoupling State
2006–2007	0.48	Weak decoupling	0.5	Weak decoupling	1.06	Expansive coupling
2007–2008	0.41	Weak decoupling	0.45	Weak decoupling	1.1	Expansive coupling
2008–2009	2.83	Expansive negative decoupling	3.37	Expansive negative decoupling	1.19	Expansive coupling
2009–2010	0.20	Weak decoupling	0.16	Weak decoupling	0.8	Weak decoupling
2010–2011	0.90	Expansive coupling	0.96	Expansive coupling	1.06	Expansive negative decoupling
2011–2012	1.43	Expansive negative decoupling	1.39	Expansive negative decoupling	0.98	Expansive coupling
2012–2013	1.56	Expansive negative decoupling	1.38	Expansive negative decoupling	0.88	Expansive coupling
2013–2014	0.91	Expansive coupling	0.87	Expansive coupling	0.95	Weak decoupling
2014–2015	10.63	Expansive negative decoupling	9.79	Expansive negative decoupling	0.92	Expansive coupling
2015–2016	1.19	Expansive coupling	1.16	Expansive coupling	0.97	Expansive coupling
2016–2017	0.42	Weak decoupling	0.32	Weak decoupling	0.75	Weak decoupling
2017–2018	0.12	Weak decoupling	0.08	Weak decoupling	0.69	Weak decoupling
2018–2019	0.73	Weak decoupling	0.59	Weak decoupling	0.81	Weak decoupling
2019–2020	1.80	Expansive negative decoupling	2.14	Expansive negative decoupling	1.19	Weak decoupling

. The correlation between energy consumption and GDP, along with the connection between carbon dioxide emissions and economic growth, is consistently correlated. Specifically, for the periods 2006–2007, 2007–2008, 2009–2010, 2016–2017, 2017–2018, and 2018–2019, the associations between energy consumption and GDP, as well as between carbon dioxide emissions and GDP, were characterized as weak decoupling. During the periods 2008–2009, 2011–2012, 2012–2013, 2014–2015, and 2019–2020, both relationships were marked by expansive negative decoupling. For the periods 2010–2011, 2013–2014, and 2015–2016, both relationships were described as expansive coupling (Table 3). A general drop was observed in the index that measures the disparity among energy consumption and carbon dioxide emissions. The findings in Table 3 show that over the study period (2006–2013), the prevailing condition of decoupling was characterized by expansionary coupling. Nevertheless, there was a subsequent transition towards a predominantly weak decoupling state over the later phase of the study period (2013–2020).

3.2. The Decoupling Relationships across Economic Sectors

Throughout the research period, the separation of carbon dioxide emissions and GDP in the primary sector was primarily identified by the presence of both mild and significant decoupling. The correlation of energy consumption and GDP in this sector showed considerable variability, with six cases of weak decoupling, four cases of expansive coupling, two cases of expansive negative decoupling, and two cases of strong decoupling. Primary-sector carbon dioxide emissions were strongly negatively decoupled from energy consumption only in 2014–2015 and in 2017–2018, with other periods characterized mainly by strong or weak decoupling. In secondary industries, decoupling of carbon dioxide emissions from GDP and decoupling of energy consumption from GDP varied considerably. The periods 2006–2007 and 2010–2011 were characterized by expansive coupling; 2007–2008, 2009–2010 and 2016–2018 were characterized by weak decoupling; and 2008–2009, 2014–2015 and 2019–2020 were characterized by strong negative decoupling, with other periods showing predominantly expansive negative decoupling. During the study period, the correlation between carbon dioxide emissions and energy consumption in the secondary sector was predominantly characterized by a significant negative decoupling. This was evident in the expansive coupling observed between 2006 and 2007 and between 2009 and 2012; however, there were also instances of weak decoupling, as observed in the period between 2012 and 2013. The tertiary sector displayed a comparable pattern of decoupling between carbon dioxide emissions and GDP, as well as between energy consumption and GDP. Weak decoupling was observed in the years 2006–2007, 2008–2014, and 2016–2017; 2007–2008 was expansive coupling, and 2014–2015 was expansive negative decoupling. A negative decoupling between carbon emissions and GDP was observed between 2015 and 2016, while a positive coupling was observed between energy consumption and GDP. Between 2017 and 2020, there was a strong decoupling between carbon emissions and GDP, while the coupling between energy consumption and GDP was found to be relatively weak or strong, depending on the case. During the study period, there was a predominant weak decoupling of carbon dioxide emissions and energy consumption in the tertiary sector (2006–2007, 2008–2011, and 2012–2014). Conversely, in the 2007–2008, 2011–2012, 2014–2015, and 2016–2017 periods, there was an expansive coupling of these variables. In the 2015–2016 period, expansive negative decoupling was observed, while in the 2019–2018 period, strong decoupling was evident.

3.3. The Decoupling Relationships across Aspects of the Secondary Sector

The secondary sector, which largely comprises manufacturing and industrial production, is traditionally energy-intensive and a significant contributor to carbon emissions. From an economic development perspective, the secondary sector is often central to economic growth, particularly in developing countries transitioning from a primary industry-based economy. Therefore, achieving decoupling in the secondary sector is crucial for sustainable economic development that does not compromise environmental integrity. Our analysis revealed marked differences in the decoupling indices for energy consumption and GDP within the secondary sector across two periods: 2006–2012 and 2013–2020. For instance, the “Mining and Washing of Coal” sector shifted from expansive negative decoupling in 2006–2012 to strong decoupling in 2013–2020, indicating a significant reduction in the energy intensity of economic output. Conversely, the “Manufacture of Furniture” sector exhibited expansive negative decoupling in the latter period, a substantial change from the recessive decoupling observed in earlier years. In terms of carbon dioxide emissions and GDP, similar variations in performance across different aspects of the secondary sector were observed. The “Extraction of Petroleum and Natural Gas” sector consistently showed strong decoupling in both periods. In contrast, the “Manufacture of Metal Products” sector demonstrated strong decoupling from 2006–2012, but this shifted to expansive negative decoupling in 2013–2020, highlighting increased emissions relative to GDP. Furthermore, when examining the combined index of energy consumption and carbon dioxide emissions, several sectors showed a transition towards more sustainable practices. Notably, the “Oil Processing, Coking, and Nuclear Fuel Processing” sector improved from expansive negative decoupling to expansive coupling, indicating a better alignment of energy use and emissions with economic growth. However, some sectors, such as the “Manufacture of General-Purpose Machinery”, moved towards less favorable strong negative decoupling, demonstrating a disconnect between economic growth and its environmental impact.

3.4. Spatial Variation of the Decoupling Relationships

Studying the spatial variation in the decoupling relationship among economic growth, energy consumption, and carbon dioxide emissions is crucial as it highlights the heterogeneity of regional development. Different regions possess unique energy mixes, industrial structures, policy environments, and technological advancements, all of which influence their energy efficiency and emissions intensity. Spatial analyses can reveal these disparities, enabling the formulation of tailored and more effective environmental and economic policies. We analyzed the decoupling relationship among GDP, energy consumption, and carbon dioxide emissions of prefectures and cities in Xinjiang Province over three stages: 2006–2010, 2010–2015, and 2016–2020. The investigation showed that from 2006 to 2010, Urumqi City, the Aksu Region, the Kashgar Region, and the Hotan Region exhibited weak decoupling between GDP and energy consumption. Conversely, Karamay City, Hami City, Ili Kazakh Autonomous Prefecture, the Tacheng Region, and Bayingolin Mongol Autonomous Prefecture displayed expansive coupling (Figure 2). Turpan City, Changji Hui Autonomous Prefecture, the Altay Region, Bortala Mongol Autonomous Prefecture, and Kizilsu Kirghiz Autonomous Prefecture experienced expansive negative decoupling. Significant changes were observed from 2011 to 2015: Urumqi City, the Aksu Region, and the Kashgar Region transitioned from weak decoupling to strong decoupling. Ili Kazakh Autonomous Prefecture, the Tacheng Region, and Bayingolin Mongol Autonomous Prefecture shifted from expansive coupling to strong decoupling. Changji Hui Autonomous Prefecture, the Altay Region, and Bortala Mongol Autonomous Prefecture moved from expansive negative decoupling to strong decoupling. Karamay City changed from expansive coupling to recessionary decoupling, and Turpan City shifted from expansive negative decoupling to recessionary decoupling. Kizilsu Kirghiz Autonomous Prefecture moved from expansive negative decoupling to weak decoupling, while the Hotan Region and Hami City remained unchanged (Figure 2). From 2016 to 2020, Urumqi City’s decoupling relationship between GDP and energy consumption shifted from strong decoupling to expansive coupling. Karamay City moved from recessionary decoupling to weak decoupling, and Turpan City transitioned from recessionary decoupling to expansive coupling. Hami City changed from expansive coupling to strong decoupling. The Tacheng Region, Altay Region, and Aksu Region demonstrated a notable shift from a state of strong decoupling to that of expansive negative decoupling. The Bortala Mongol Autonomous Prefecture exhibited a transition from weak decoupling to expansive negative decoupling, while the Ili Kazakh Autonomous Prefecture demonstrated a shift from strong decoupling to weak decoupling. There were no changes observed in Changji Hui Autonomous Prefecture, Kizilsu Kirghiz Autonomous Prefecture, the Hotan Region, and Bayingolin Mongol Autonomous Prefecture (Figure 2).

The spatial variation analysis of the decoupling relationship between carbon dioxide emissions and GDP from 2006 to 2010 revealed diverse patterns across regions. Turpan City, Changji Hui Autonomous Prefecture, the Altay Region, Bortala Mongol Autonomous Prefecture, Bayingolin Mongol Autonomous Prefecture, and Kizilsu Kirghiz Autonomous Prefecture were classified as exhibiting expansive negative decoupling. Karamay City, Hami City, and the Tacheng Region showed expansive coupling, while the Hotan Region experienced strong decoupling. Urumqi City, Ili Kazakh Autonomous Prefecture, Aksu Region, and the Kashgar Region displayed weak decoupling (Figure 3). From 2011 to 2015, there were notable shifts in the observed patterns. In Urumqi City, there was a shift in the nature of coupling, from a state of weak decoupling to one of strong decoupling. Similarly, in Karamay City there was a shift in the coupling state, moving from expansive coupling to strong negative decoupling. Finally, Turpan City exhibited a shift in coupling, moving from expansive negative decoupling to a state of strong negative decoupling. Hami City underwent an evolution from expansive coupling to expansive negative decoupling, while the Tacheng Region shifted from expansive coupling to weak decoupling. The Altay Region exhibited an improvement from expansive negative decoupling to strong decoupling, while Bortala Mongol Autonomous Prefecture, Bayingolin Mongol Autonomous Prefecture, and Kizilsu Kirghiz Autonomous Prefecture demonstrated a shift from expansive negative decoupling to weak decoupling. The decoupling status of Changji Hui Autonomous Prefecture, the Aksu Region, the Kashgar Region, the Hotan Region, and Ili Kazakh Autonomous Prefecture remained unchanged (Figure 3). From 2016 to 2020, further transitions occurred. The cities of Urumqi, Karamay, and Turpan exhibited differing patterns of decoupling over the course of the study period. Urumqi City shifted from a state of strong decoupling to one of weak decoupling, while Karamay City underwent a transition from strong negative decoupling to weak decoupling, and Turpan City experienced a shift from strong negative decoupling to strong decoupling. Ili Kazakh Autonomous Prefecture and Bortala Mongol Autonomous Prefecture shifted from weak decoupling to expansive coupling. The Tacheng Region underwent a transition from a state of weak decoupling to a more pronounced one, while Bayingolin Mongol Autonomous Prefecture and the Kashgar Region exhibited a shift from a state of weak decoupling to one of expansive negative decoupling. The Hotan Region underwent a transition from a state of strong decoupling to that of expansive negative decoupling. Hami City, the Altay Region, the Aksu Region, Kizilsu Kirghiz Autonomous Prefecture, and Changji Hui Autonomous Prefecture experienced no change in their decoupling status during this period (Figure 3).

Further analyses of the decoupling relationship between carbon dioxide emissions and energy consumption from 2006 to 2010 revealed that Turpan City, Changji Hui Autonomous Prefecture, the Altay Region, Bortala Mongol Autonomous Prefecture, and Kizilsu Kirghiz Autonomous Prefecture exhibited expansive negative decoupling. In Urumqi City, Karamay City, Hami City, Ili Kazakh Autonomous Prefecture, the Tacheng Region, and the Aksu Region, expansive coupling was observed. Conversely, the Hotan Region exhibited a pronounced degree of decoupling, while Bayingolin Mongol Autonomous Prefecture and the Kashgar Region demonstrated a relatively weak decoupling (Figure 4). From 2011 to 2015, significant shifts occurred: Karamay City, Ili Kazakh Autonomous Prefecture, the Aksu Region, and the Tacheng Region transitioned from expansive coupling to strong negative decoupling. Bayingolin Mongol Autonomous Prefecture and the Kashgar Region have undergone a transition from a state of weak decoupling to a more pronounced state of negative decoupling. The cities of Urumqi, Turpan, and Hami demonstrated changes in coupling patterns. Urumqi shifted from expansive coupling to weak negative decoupling, Turpan shifted from expansive negative decoupling to strong negative decoupling, and Hami shifted from expansive coupling to expansive negative decoupling. In contrast, Changji Hui Autonomous Prefecture exhibited a shift from expansive negative decoupling to strong negative decoupling, while the Altay Region demonstrated a shift from expansive negative decoupling to a recessionary coupling pattern. No changes were observed in Bortala Mongol Autonomous Prefecture, Kizilsu Kirghiz Autonomous Prefecture, and the Hotan Region (Figure 4). The dominant decoupling status across various prefectures and cities was strong decoupling throughout the 2016–2020 period. The status of Urumqi City and the Aksu Region shifted from that of weak negative decoupling to that of weak decoupling. Similarly, Karamay City and Ili Kazakh Autonomous Prefecture shifted from that of strong negative decoupling to that of expansive negative decoupling. In contrast, the status of Turpan City and the Tacheng Region shifted from that of strong negative decoupling to that of strong decoupling. Furthermore, the status of Hami City shifted from that of expansive negative decoupling to that of strong negative decoupling. The Altay Region transitioned from a state of recessionary coupling to a state of strong decoupling. Bortala Mongol Autonomous Prefecture transitioned from a state of expansive negative decoupling to a state of weak decoupling. The Hotan Region transitioned from a state of strong decoupling to a state of expansive negative decoupling. No changes were observed in Changji Hui Autonomous Prefecture, Bayingolin Mongol Autonomous Prefecture, Kizilsu Kirghiz Autonomous Prefecture, and the Kashgar Region (Figure 4).

4. Discussion

4.1. Key Findings

The decoupling analysis of economic development, energy consumption, and carbon dioxide emissions in Xinjiang from 2006 to 2020 reveals evolving dynamics over the period. During this time, Xinjiang experienced rapid economic growth alongside a more gradual rise in energy consumption and carbon dioxide emissions. The findings indicate distinct decoupling patterns across different periods and sectors. Initially, expansive coupling was prevalent, signifying a phase where economic growth was heavily dependent on increased energy consumption and emissions. However, from around 2010 onwards, a shift towards weak decoupling became evident, progressing towards more robust forms of decoupling in subsequent years. The primary sector primarily exhibited weak and strong decoupling. In contrast, the secondary sector displayed fluctuating decoupling states, reflecting the variability in energy and emissions intensity across industrial activities. The tertiary sector generally showed weak decoupling. The spatial analysis of Xinjiang’s prefectures and cities highlighted significant regional variations, emphasizing the role of local economic structures and policies in influencing decoupling outcomes. These findings underscore the importance of region-specific strategies to achieve sustainable development.

The results provide robust validation of the hypotheses posited in the introduction, elucidating the intricate relationship between economic growth, energy utilization, and environmental impact in Xinjiang. The first hypothesis, predicting a favorable decoupling status due to ecological civilization construction initiatives, is supported by the overall data showing a decline in energy consumption and carbon dioxide emissions relative to GDP growth. The second hypothesis, anticipating variations in decoupling across economic sectors, is clearly confirmed through the analysis, which underscores significant sectoral differences in their interactions with decoupling processes. The third hypothesis, foreseeing spatial differences in decoupling across regions, is substantiated by distinct decoupling profiles observed among Xinjiang’s prefectures and cities. These variations underscore the necessity for tailored regional strategies that address specific economic and environmental conditions, thereby promoting more effective and sustainable growth. Thus, the hypotheses not only align with the observed data but also frame critical factors influencing the pathway toward sustainable development in Xinjiang.

4.2. Spatiotemporal Variation in Decoupling Relationship

The spatial analysis of decoupling relationships in Xinjiang highlights significant regional differences in how economic growth, energy consumption, and carbon dioxide emissions interact. These variations are reflective of the diverse economic structures, resource endowments, and policy environments across different prefectures and cities within the province. For instance, industrial cities like Urumqi and Karamay exhibit different decoupling patterns compared to more agriculturally based regions such as Hotan and Aksu. Such disparities underscore the importance of understanding local contexts when assessing and implementing sustainability measures. As reported previously, regional economic policies tailored to the specific characteristics of each region can significantly enhance the effectiveness of decoupling strategies, thereby promoting more sustainable development outcomes [50,51,52].

Moreover, the study period reveals shifts in the decoupling status of these regions, indicating the dynamic nature of economic–environmental interactions. Early in the study period, many regions showed expansive coupling or weak decoupling, suggesting that economic growth was heavily reliant on increases in energy consumption and associated emissions. However, as policies targeting enhanced energy efficiency and greater use of renewable energy sources were implemented, several regions exhibited transitions towards stronger forms of decoupling. This evolution aligns with findings by Ma [53], who document similar transitions in other rapidly developing areas of China, attributing these changes to proactive environmental governance and technological advancements in energy production and usage.

The spatial decoupling analysis also illustrates the critical role of technological adoption and policy enforcement in shaping decoupling outcomes. Regions that were more proactive in adopting new technologies and enforcing strict environmental regulations showed more favorable decoupling states by the end of the study period. This is consistent with the previous work which found that technological innovation and stringent environmental policies are pivotal in decoupling economic growth from environmental degradation [54]. As Xinjiang continues to develop, maintaining a focus on regional specificities will be essential for crafting policies that not only promote economic growth but also safeguard environmental integrity. These findings suggest that a nuanced, region-specific approach will be essential for achieving sustainable development [55,56].

Regions in Xinjiang vary significantly in terms of natural resource endowments. For instance, areas rich in fossil fuels, such as Karamay, have traditionally relied heavily on energy-intensive industries. This dependency makes achieving decoupling more challenging compared to regions with abundant renewable energy resources, such as Hami, which have greater potential for sustainable energy development. The economic structure of each region also plays a pivotal role. Prefectures with a diversified economy, including a mix of agriculture, industry, and services, tend to show better decoupling outcomes. For example, Urumqi, with its diverse economic activities and a strong service sector, has exhibited relative decoupling in recent years. In contrast, regions heavily reliant on a single industry, such as Turpan with its focus on viticulture, may face difficulties in achieving decoupling without significant economic restructuring. Technological advancements and the adoption of clean technologies are also critical for decoupling. Regions with higher technological capabilities, such as those with research institutions and higher education centers, are better positioned to implement energy-efficient and low-carbon technologies. For instance, Urumqi and Shihezi, with their universities and research centers, have shown better progress in decoupling compared to less technologically advanced regions.

4.3. Discrepancies among Different Sectors

The decoupling analysis across different sectors within Xinjiang presents a varied landscape of how economic activities interact with energy consumption and carbon dioxide emissions. In the primary sector, which typically includes agriculture and extraction industries, there is a consistent pattern of either weak or strong decoupling. This sector’s relatively lower energy requirements and direct dependence on natural resources make it more responsive to improvements in practices and technologies that reduce environmental impact. Studies like those by Johnson and Geldner [57] have highlighted how advances in agricultural technology and better resource management practices can lead to significant gains in decoupling economic outputs from environmental impacts in primary industries. Conversely, the secondary sector, comprising manufacturing and industrial production, shows the most variability in decoupling states. This sector is inherently energy-intensive and historically linked to higher carbon emissions. The observed fluctuations in decoupling states—ranging from expansive negative decoupling to strong decoupling—reflect the ongoing challenges and intermittent progress in implementing efficient and sustainable industrial practices. According to previous studies, achieving consistent decoupling in industrial sectors often requires substantial investments in clean technologies, regulatory overhauls, and a shift towards less carbon-intensive methods of production, which can be difficult to implement uniformly across diverse industries [58,59,60]. The tertiary sector, which includes services and is generally less energy-intensive than the secondary sector, displayed predominantly weak decoupling. This suggests that while the sector’s energy use is not escalating as rapidly as in other sectors, there remains significant room for improvement in terms of energy efficiency and integration of sustainable practices. The work on service economies suggests that although the tertiary sector’s direct energy consumption is lower, indirect emissions through activities such as transportation, office heating, and cooling remain substantial [61,62]. Enhancing energy efficiency in these areas through smarter urban planning and greater adoption of information and communication technology can further strengthen decoupling efforts in this sector.

4.4. Policy Effectiveness and Recommendations

Since 2011, China and Xinjiang province have undertaken significant strategies to adjust and optimize their energy structures, aiming to build a safe, stable, economical, and clean modern energy industry system [63]. The implementation of these policies has facilitated the decoupling of economic growth from energy consumption and carbon emissions [64,65]. Specific measures include the development of efficient large-capacity coal-fired units, prioritizing combined heat and power in major urban centers and industrial parks, and promoting comprehensive utilization of coal gangue and large pithead power stations. These initiatives have significantly improved energy efficiency and reduced the energy consumption and carbon emissions per unit of economic output [64,66,67]. This can happen through the adoption of cleaner and more efficient technologies, which reduces the carbon intensity of economic activities; the reallocation of resources toward more efficient and environmentally friendly practices; and the development of low-carbon industries, such as services and high-tech sectors. Additionally, Xinjiang’s active development of hydropower and other renewable energy sources not only optimizes the energy structure but also reduces reliance on fossil fuels, thereby achieving better environmental decoupling in regional economic growth [32]. The dual-carbon goals introduced by the Chinese government in 2021, aimed at achieving carbon peak and carbon neutrality, signify a major commitment to climate change action [68]. This strategy demands an accelerated pace of decarbonization and an economic transition from high-carbon to low-carbon models, influencing domestic and international policies, industrial upgrades, and global climate governance profoundly [69,70]. In response, China has increased investments in clean energy technologies, such as solar power, wind energy, and electric vehicles, which are key to achieving carbon neutrality.

The Chinese government implemented the National Medium- and Long-term Plan for Hydrogen Energy Industry Development in 2023, it is essential to focus on regions with new favorable energy resources, low electricity costs, and advanced hydrogen infrastructure to scale up green hydrogen production [71]. This involves integrating production, storage, and usage of green hydrogen, particularly in sectors like transportation and chemicals. Companies producing significant quantities of green hydrogen should receive incentives, such as allocations of renewable energy based on their operational electricity use, promoting self-sufficiency. Additionally, integrating renewable energy in the oil and gas industry, as outlined in the “Action Plan for Accelerating the Exploration, Development, and Integration of Oil and Gas with New Energy (2023–2035)”, encourages major operators to replace fossil fuel consumption with renewable electricity during exploration and processing phases. This shift supports the clean and low-carbon transformation of energy used in these industries. Further, promoting the development of green, low-carbon industrial parks by integrating renewable energy projects, or green transformation of the building industry [72], ensures that new materials industries adhere to high environmental standards. These combined strategies will prove instrumental in enhancing energy efficiency, reducing carbon emissions, and fostering a sustainable economic environment across various sectors.

5. Conclusions

This study conclusively demonstrates that Xinjiang has made significant progress in decoupling economic growth from energy consumption and carbon dioxide emissions between 2006 and 2020. This achievement is largely due to the strategic implementation of policies aimed at optimizing energy structures and promoting sustainable practices across various sectors. The spatial analysis reveals significant regional variations in decoupling outcomes, influenced by local economic structures, resource endowments, and policy interventions. The primary sector consistently advanced towards strong decoupling, while the secondary sector showed variable results due to its inherently high energy intensity. The tertiary sector, despite its lower overall energy consumption, still offers substantial opportunities for further decoupling through technological advancements and operational improvements.

The findings underscore the critical need for region-specific strategies tailored to accommodate the distinct characteristics and developmental stages of various areas within Xinjiang. Such an approach is essential to achieve more substantial and consistent decoupling outcomes in the future. Moreover, this study highlights the ongoing importance of continuous policy innovation, adoption of advanced technologies, and international collaboration to advance China’s ecological civilization objectives and contribute to global sustainability efforts.

Looking ahead, it will be crucial to maintain a steadfast focus on integrating ecological considerations into economic planning and development strategies. This integration ensures that economic progress aligns harmoniously with environmental preservation goals. This research provides valuable insights for policymakers, stakeholders, and scholars interested in understanding the interplay between economic development dynamics and environmental sustainability in Xinjiang and similar regions worldwide.

This research provides valuable insights for policymakers, stakeholders, and scholars interested in understanding the interplay between economic development dynamics and environmental sustainability. By offering a comprehensive analysis of the spatiotemporal decoupling relationship between economic growth, energy consumption, and carbon dioxide emissions, this study contributes to the broader discourse on sustainable development and climate change mitigation strategies.