China’s Photovoltaic Development and Its Spillover Effects on Carbon Footprint at Cross-Regional Scale: Insights from the Largest Photovoltaic Industry in Northwest Arid Area

Abstract

Solar energy plays a crucial role in mitigating climate change and transitioning toward green energy. In China (particularly Northwest China), photovoltaic (PV) development is recognized as a co-benefit and nature-based solution for concurrently combating land degradation and producing clean energy. However, the existing literature on the subject is limited to the local effects of PV power station construction and ignores the spillover environmental effects in distant regions. Thus, a hotspot of PV development in Northwest China was selected as a case to quantify the spill-over impacts of PV development in Qinghai Province on cross-regional economy and the environment using an environmentally extended multi-regional input–output approach and related socioeconomic and environmental statistical data. A cross-regional carbon footprint analysis revealed that the eastern region of Qinghai Province had the highest carbon footprint, followed by the southwestern, central, southern, northwestern, northern, and northeastern regions; the production and supply sectors of electricity and heat were the primary sources of carbon emissions, followed by metal smelting and rolling processing products, non-metallic mineral products, and the transportation, warehousing, and postal sectors. In addition, the PV development in Qinghai Province strongly supports the electricity demand in the central and eastern coastal areas, while substantially reducing the carbon emissions in the eastern, southwestern, and central regions (through the distant supply of PV products). We quantified the spillover effects of PV development in Qinghai Province and address the challenges of PV development in the carbon emission reduction strategies implemented at the regional and cross-regional scales; our findings will support policymakers in developing plans that ensure sustainable energy supply and help China to achieve its carbon neutrality goals.

1. Introduction

Solar energy is an important renewable energy source for achieving carbon emission reduction. It plays a crucial role in mitigating carbon emission and accelerating energy-structure transformations, ultimately contributing to the sustainable development goals (SDGs), particularly Affordable and Clean Energy (SDG 7), Climate Action (SDG 13), and Life on Land (SDG15), proposed by the United Nations [1,2]. Several studies indicate that over the next 30 years, solar energy will account for more than 10% of global energy consumption, with an annual growth of 7.6% [3,4]. Photovoltaic (PV) power station construction is recognized as an efficient model for solar energy collection and conversion [5,6,7]. In particular, China and several other countries around the world (e.g., India, Morocco, Egypt, Sudan, and Vietnam) are conducting requisitions of cropland and unused land to allot sufficient area for PV power station construction [3,8,9]. Deserts have sufficient lighting conditions and cover extensive areas; thus, they can play a crucial role in facilitating PV development [5,6]. As of 2018, deserts provided the largest construction area (in the world) for PV development, among all other land types [7]. China accounts for 28% of the global carbon emissions [7], and PV industry development was initiated in the early 21st century, with the aim of achieving carbon neutrality [10,11]. By 2021, China ranked first in terms of the installed PV capacity [12].

The majority of existing studies regarding the PV industry focus on PV technology and its economic and environmental effects [8,9,13,14], e.g., battery pollution and disposal issues, power generation efficiency and stability, the uncertainties associated with economic benefits, and equipment safety risks [1,2]. Note that improper installation of PV power generation equipment may damage building structures, resulting in several issues, such as water leakage, collapse, and flying panels in strong winds, thereby posing risks to life and property [1,2]. In terms of the environmental effects of PV development, previous studies focused on the responses of landscape structure and ecosystem functions to PV power station construction and the associated landscape transformations, including those related to carbon sequestration, sand fixation, biodiversity maintenance, and climatic and hydrological regulation [9,10,15,16,17,18,19]. Notably, the literature is limited to the local effects of PV power station construction on land and water resources and carbon emissions, which are directly related to land use and cover and vegetation cover changes. However, the carbon footprint effects of PV power stations have not been sufficiently revealed in existing studies [7,9,10]. Carbon footprint is an indicator used to measure the amount of carbon emissions directly or indirectly caused by an individual, organization, product, or country over a certain period of time [20]. It covers the emissions throughout the entire life cycle of a product or service, from production to transportation, final use, and disposal, which enables us to more accurately understand and evaluate the impacts of human activities on the environment [20]. In terms of carbon footprint effects, PV power stations significantly reduce carbon emissions by converting solar energy into electricity, thereby reducing the consumption of fossil fuels [7]. The carbon emissions of PV power generation are estimated to be at about 10% of traditional power generation methods [7].

In general, there are close connections between the geographical and environmental variables of a region, with the surrounding and distant areas being affected in terms of energy supply, livestock products, water, and economic trade [20,21,22]. However, the teleconnections between PV power generation and distant carbon footprint (i.e., carbon emissions) have not been sufficiently quantified on cross-provincial levels under the existing frameworks for carbon footprint accounting such as life cycle assessment, energy mineral fuel emissions, and input–output analysis [7,23,24,25]. The environmentally extended multi-regional input–output analysis method is a comprehensive model that combines input–output analysis and environmental economics to evaluate the impact of economic activities on the environment [22,26]. At the macro level, the model can quantitatively analyze the teleconnections between different elements in the environmental and economic system, while accounting for various national economic sectors related to the environment (e.g., resource consumption, waste, and pollution control) [27]. This approach not only considers the balance between various sectors in traditional input–output analysis, but also accounts for important environmental factors (such as material, carbon, and water footprints), thereby advancing the understanding on the effects of economic activities on the environment and the role of economic activities in reducing negative environmental impacts [27]. This model, generally used at the national and local scales, can be extended to conduct multi-regional and telecoupling assessments at sub-national scales; thus, the models can be applied for environmental stress accounting, life cycle assessment, contribution quantification of socioeconomic factors, and industrial chain path analysis [21,28,29,30].

Qinghai Province in Northwest China contains several desert areas, such as the Qaidam and Golmud, as well as some small deserts and sandy areas; these are ideal places for building PV power stations that have seen rapid photovoltaic industry development in the past decade [11]. Therefore, for this study, we selected Qinghai Province as the target region. The aim of this study was to investigate the power generation structure changes from 1995 to 2021 in Qinghai Province and other regions of China on provincial level, and then to test hypothesis that whether the PV development in Qinghai Province presented spillover impacts on the economy and environment at the cross-regional scale, with respect to distant energy transmission. Accordingly, the objectives of this study were to (i) reveal the evolution of the energy structure of China from 1995 to 2021, focusing on the structural composition of traditional thermal power and PV energy; (ii) identify the cross-regional carbon footprint of Qinghai Province and determine its teleconnections with the carbon emissions in other regions of China and sectors; and (iii) examine the spillover effects of PV development in Qinghai Province on the carbon emissions at the cross-regional scale and assess the associated implications on the energy structure transformation and adjustment in the region. Our study can support policymakers in developing effective plans for sustainable energy supply, while advancing PV development in China’s desert areas and facilitating carbon neutrality goals.

2. Materials and Methods

2.1. Study Area

Qinghai Province is located in Northwest China, with a geographical location between 89°35′–103°04′ E and 31°9′–39°19′ N. Its total area is 72.23 km², accounting for one thirteenth of China’s total area and ranking fourth among all provinces, municipalities, and autonomous regions. Qinghai Province is located on the Qinghai–Xizang Plateau and has a plateau continental climate. Its climate characteristics are long sunshine hours and strong radiation. The average annual radiation reaches 5860–7400 MJ/m² with sunshine hours ranging from 2336 to 3341 h, indicating abundant solar energy resources. Qinghai Province has a diverse range of land types, from west to east, with glaciers, Gobi, deserts, grasslands, water bodies, forests, and cropland distributed in a trapezoidal pattern. The eastern agricultural region has formed three-dimensional terraces of rivers, shallows, and brains, with scattered plots that are difficult to develop and intensively utilize in contiguous areas. The total area of desertified land in Qinghai Province is 12.50 × 10⁴ km², accounting for 17.4% of the province’s total land area. It is one of the provinces with the highest altitude and most serious desertification in China.

As an important new energy production base in China, Qinghai Province has abundant wind and solar resources and superior conditions for the development of PV power generation. By 2023, the proportion of new energy generation in Qinghai Province exceeded 50% for the first time, becoming the largest power source in the province. This evidences remarkable progress in building a new energy-based power system. Qinghai Province has built a large number of PV power stations with a cumulative power generation of 5.85 × 10¹⁰ kWh, demonstrating the positive role of PV power generation in serving people’s livelihoods and promoting local economic development [31,32]. In the economic structure of Qinghai Province in 2023, the proportion of the primary, secondary, and tertiary industries was 10.2%, 42.4%, and 47.4%, respectively, indicating that the tertiary industry occupies the largest proportion in the economic structure, followed by the secondary industry and primary industry.

2.2. Data Sources

The MRIO tables for Qinghai Province (2012 and 2017) and entire country (2012 and 2017), including more than 31 provinces, autonomous regions, and municipalities, were collected from Bureau of Statistics of Qinghai Province and Zheng et al. (2020, 2021) [33], respectively. The data for Hong Kong, Macau, and Taiwan are not included because of different statistical standards and classification systems. The Chinese provincial multi-regional input–output database for 2012, 2015, and 2017 complied by Zheng et al. (2021) [34] has been widely applied in worldwide carbon footprint, land footprint, water footprint, and other resource flow analyses [35,36,37]. Other socioeconomic (e.g., scale of PV industry and its power generation), energy structure, and carbon emission statistical records (from 1995 to 2021) at provincial level were derived from the China Statistical Yearbook (1996–2022) [38].

2.3. Environmentally Extended Multi-Regional Input–Output (EE-MRIO) Analysis

Input–output analysis (IOA) is an economic analysis theory used to describe the supply and demand relationships and economic interactions between different industries or sectors. Note that IOA expresses the interdependence between various sectors within a regional economy in a matrix form [26]. Its basic idea is that the output of any sector, although based on its own production activities, also depends on the input of other sectors; this input–output relationship between different sectors can form a complex economic interaction network [39]. In practical applications, IOA is used to explore the resource utilization efficiency [40], environmental impact [22], and industrial linkage strength at the regional scale [41]. The IOA model relies on detailed economic activity data and analyzes the flow of resources and products between various economic sectors by constructing input–output tables.

In the IOA model, “input” and “output” are the fundamental elements for analyzing the interactive effects between various sectors within an economic system [26]. In this model, “input” refers to the resources (such as raw materials, labor, capital equipment, and services) that are necessary for the production of goods and services. In the input–output table, these resources are subdivided into intermediate inputs from other industrial sectors and primary inputs from outside the production sector, such as labor, capital, and land. The investments include not only material resources, but also technology, energy, and other operating costs, all of which are consumed during the production process. “Output” refers to the goods or services produced by various sectors through the conversion of inputs. In the model, the output of each sector is supplied as the final product to consumers, including households, governments, and exports, and as an intermediate product to other industrial sectors, representing the economic activity in an economic sector over a certain period of time. The IOA model reveals the complex interdependence between various sectors within an economy system, supporting economists, policymakers, and businesses in understanding the transmission mechanism of economic changes, quantify the potential impact of economic policies, and optimize resource allocation to enhance economic activity efficiency and the relative competitiveness within a sector [30].

Multi-regional input–output (MRIO), an extension of the IOA approach, expands the scope of analysis from a single regional economy to multiple geographic spaces. By tracking the flow of goods and services between regions, the model can identify the economic interactions between multiple geographic regions or countries [29]. On this basis, integrating environmental indicators, such as carbon emissions, energy consumption, and water resource use, an environmentally extended multi-regional input–output (EE-MRIO) theoretical framework was constructed to clarify the direct and indirect impacts of regional economic activities on the environment, with the aim of balancing the relationship between economic growth, resource consumption, and environmental impact, and providing scientific basis for formulating regional sustainable development strategies [28,34]. In this study, the EE-MRIO approach was applied based on MATLAB platform to quantify the cross-regional impacts of PV development. The basic structure of the EE-MRIO model used in this study is shown in Table 1.

The EE-MRIO table used in this study integrated the economic and environmental data of the target region. The rows of the matrix represent the sectors that provide goods or services, while the list represents the sectors or end-users that consume these goods or services. Theoretically, in a closed economic system, the total input of all sectors would be equal to the total output, because without external input and output, all production and consumption activities will circulate within the economic system [42]. This balance relationship forms the foundation of MRIO analysis, helping analysts understand the operational mechanisms within the economy and the interdependence between various sectors [43]. Note that E_m is a key element of an EE-MRIO table; it characterizes the intensity of the impact of sectoral economic activities on the environment, such as sectoral carbon emissions [44], water resource consumption [45], energy consumption [20], and waste generation [21]. It helps researchers track the cascading response of specific regional economic activities to the associated local and industrial upstream and downstream regions [22]. The inter-regional sectors generally follow a balance relationship, as shown in Equation (1).

(1)$X=Z+Y=BX+Y$

where X is the total output vector, representing the total output of each sector in all the study regions; Z is the intermediate demand matrix, representing the total quantity of goods and services provided by different sectors for mutual production activities; Y denotes the final demand vector, representing the final demands of household, government, capital formation, export, and other categories in various sectors of the study region; and B denotes the technical coefficient matrix (i.e., direct input coefficient matrix), representing the intermediate input from other sectors that are necessary for the output of a sector unit. This equation forms the core of the EE-MRIO model and can be used to understand the dependency between various sectors of the economy and analyze how economic activities are driven by the final demand [27].

(2)$X={(I-B)}^{-1}Y=LY$

where L indicates the Leontief inverse matrix (dimensionless), used to analyze the increase in the output in each sector in the regional economic system required to meet the final demand of a unit; its reflects the indirect interdependence between all the sectors and the need for multiple rounds of reproduction activities. Based on the above equation of inter-regional trade relations, we used environmental impact variables to calculate the carbon and PV product footprints between regions.

(3)$E_c={\widehat{e}}_c{\left(I-B\right)}^{-1}Y={\widehat{e}}_{c}LY$

(4)${\widehat{e}}_c=C_{ij}/x_{ij}$

(5)$E_p={\widehat{e}}_p{\left(I-B\right)}^{-1}Y={\widehat{e}}_{p}LY$

(6)${\widehat{e}}_c=\left\{\begin{array}{c}{P}_i/x_{ij},j=24\\ 0,j\ne 24\end{array}\right.$

where E_c indicates the carbon footprint vector, representing the total carbon emissions from each sector in the production process between regions. C_ij denotes the carbon emissions of sector j in province i, x_ij denotes the total output of sector j in province (or autonomous region or municipality) i, and ${\widehat{e}}_c$ denotes the carbon emission coefficient matrix of each sector in each region, representing the corresponding carbon emissions per unit output of the sector. E_p denotes the footprint vector of PV products, representing the total flow of PV products in each regional power sector with regional trade. P_i is the PV power generation in province (or autonomous region or municipality) i, and ${\widehat{e}}_p$ is the production coefficient matrix of PV products in each regional power sector, representing the corresponding PV power generation per unit output of each regional power sector. Based on the above equations, we assessed the impact of PV development on the regional economy and environment under the MRIO framework, and the contribution of PV development to the regional carbon reduction was calculated based on the substitution of traditional thermal power generation by PV power generation [46]. This study presents a detailed analysis of the PV development in Qinghai Province at the provincial sector level.

3. Results

3.1. Power Generation Structure of China from 1995 to 2021

China’s power generation has increased gradually from 1995 to 2021 (Figure A1). During this period, China’s economy experienced rapid growth, with a substantial increase in demand for electricity. The accompanying industrialization and urbanization processes led to a large increase in electricity consumption, and the expansion of industrial production, infrastructure construction, and service industries also demanded a large amount of electricity supply. The increase in urban population and improvement in the residents’ living standards brought about by economic growth also led to more electricity demand from residents, resulting in an increase in the household electricity consumption across the country. From 1995 to 2007, the thermal power generation in China accounted for an average of 80% of the total electricity generation. In 2007, the proportion of thermal power generation reached the highest share of 82.98%. As of 2021, the top five provinces and autonomous regions in China, in terms of power generation, were Guangdong, Shandong, Inner Mongolia, Jiangsu, and Xinjiang (6306.23 × 10⁸, 6210.32 × 10⁸, 6119.93 × 10⁸, 5968.89 × 10⁸, and 4683.55 × 10⁸ kWh, respectively) (Figure A2); compared with 1995, the power generation increased by 668.02%, 740.11%, 2097.12%, 752.24%, and 3789.45%, respectively. Inner Mongolia and Xinjiang, located in Northwest China, have substantially increased their power generation. Guangdong and Jiangsu are both economically developed provinces, with highly concentrated industries and manufacturing, resulting in huge electricity demand. To meet this demand, the government has provided strong support for infrastructure construction and energy supply. Shandong is a major industrial province in China, especially with respect to heavy industry, resulting in huge electricity demand [47,48,49]. Inner Mongolia and Xinjiang have abundant reserves of coal, oil, and natural gas resources; therefore, they are important energy bases.

The implementation of the Western Development Strategy promoted the development of local infrastructure and energy projects across China. However, the large-scale thermal power generation in the country poses serious environmental issues, including air pollution and greenhouse gas (GHG) emissions [50,51]. To address these issues, China has gradually adjusted its energy structure and continuously increased the proportion of clean energy generation. As of 2021, based on the trend of increasing total power generation, the share of thermal power generation in China’s energy mix has reduced to 62.46%. This trend also reflects China’s efforts toward energy transformation and sustainable development [52].

China’s PV power generation industry is relatively mature; the industry has experienced rapid development in the past decade. As shown in Figure A3, from 2015 to 2020, the number of PV power stations in China increased from 1637 to 10,947, with the majority of them being concentrated to coastal areas, with the most significant increase being noted in Qinghai and Ningxia [9]. Qinghai and Ningxia are located on plateaus and inland areas; they receive abundant sunshine for extended hours and have abundant solar energy resources. Furthermore, the two regions have vast deserts and areas of unused land, which can be used to build large-scale PV power generation bases; moreover, the land price is relatively low, which is conducive to strengthening the scale of the PV industry. Thus, they are suitable for the development of the PV industry. In addition, by developing the PV industry, land desertification can be reduced to a certain extent, and the eco-environment of these regions can be improved as well [53]. Through policy support and large-scale project construction, a large increase in PV consumption area can be achieved [31,32,54].

As shown in Figure 1 and Figure 2, Guangdong, Shandong, Inner Mongolia, Jiangsu, and Xinjiang are the regions with the highest total power generation in China. However, from the perspective of PV power generation, Shandong, Hebei, Inner Mongolia, and Qinghai are the highest producers, with the total PV power generation in 2021 being 310.45 × 10⁸, 279.32 × 10⁸, 211.90 × 10⁸, 210.73 × 10⁸, and 195.32 × 10⁸ kWh, respectively, with an increase of 1375.21%, 2850.33%, 272.45%, 190.34%, and 910.27% compared to the power generated in 2015. Among them, Shandong, Hebei, and Qinghai have the highest growth rate, clearly demonstrating the progress of energy transformation in Shandong, Inner Mongolia, and Jiangsu. Inner Mongolia has abundant coal resources; therefore, the region is the top thermal power producer in China. The vast land and abundant solar and wind resources in Inner Mongolia are also very suitable for the development of renewable energy. Through the implementation of “Wind Energy and Solar Energy Complementation” projects and the construction of large-scale PV power generation bases, Inner Mongolia has made substantial progress in the field of clean energy [55]. Shandong is a major economic province in China, with developed industries and huge demand for electricity. To meet the huge electricity demand, Shandong actively promotes the development of clean energy. The government has formulated a series of policies and plans to promote the development of clean energy and encourage enterprises and social organizations to invest in PV power generation projects, especially in rural areas where the “Poverty Alleviation through Photovoltaic Development” project is promoted, to optimize the regional energy structure [56].

3.2. Cross-Regional Carbon Footprint Assessment

China is the world’s largest energy consumer and carbon dioxide emitter, accounting for about 30% of global carbon emissions. According to publicly available data from the China Carbon Accounting Database (Figure A4), in 2021, the carbon emissions of Shandong, Hebei, Inner Mongolia, and Jiangsu were 947.24 × 10⁶, 885.51 × 10⁶, 843.42 × 10⁶, and 817.70 × 10⁶ t, respectively (recording the highest emission in China). As shown in Figure 3, the carbon emissions of the four provinces mainly were mainly from the production and supply of electricity and heat (S24), followed by those from metal smelting and rolling processed products (S14), non-metallic mineral products (S13), and transportation, warehousing, and postal services (S29). These high carbon emissions were mainly from coal-fired power generation and the related sectors in the coal industry chain. The carbon emissions from the S24 sector in Shandong, Hebei, Inner Mongolia, and Jiangsu provinces accounted for 61.16%, 38.63%, 77.16%, and 56.36% of the total carbon emissions, respectively. The carbon emissions from the S14 sector accounted for 15.01%, 42.57%, 5.00%, and 22.75% of the total carbon emissions, while those from the S13 sector accounted for 7.52%, 4.83%, 6.70%, and 6.49% of the total carbon emissions. The carbon emissions of the S29 sector accounted for 3.78%, 1.60%, 1.98%, and 5.61% of the total carbon emissions, respectively. Thermal power generation is one of the main sources of carbon emissions, and coal mining also releases GHG (such as methane), exacerbating the issue of carbon emissions. Large amounts of coal and electricity are consumed in the processes of metal smelting and rolling processing and the manufacturing of non-metallic mineral products, resulting in high carbon emissions; this is especially applicable to cement production, which requires high-temperature calcination and also relies on a large amount of fossil fuel [57]. The carbon emissions of the transportation, warehousing, and postal sectors are mainly from the use of fuel. With the growth in economic activities and logistics demand, carbon emissions are likely to increase [58]. Reducing carbon emissions, especially by decreasing the reliance on coal-fired power generation, is the key to achieving carbon neutrality. In the process, it is necessary to develop renewable energy, improve energy utilization efficiency, promote clean production technologies, and promote energy structure adjustment through policies and market mechanisms.

By using the EE-MRIO approach, we calculated the cross-regional inflow and outflow of carbon footprint in Qinghai Province (Figure 4a). The carbon footprint of Qinghai was the highest in the eastern region (2977.13 × 10⁶ t), followed by the southwestern, central, southern, northwestern, northern, and northeastern regions (1939.01 × 10⁶, 1547.20 × 10⁶, 1060.05 × 10⁶, 859.16 × 10⁶, 788.03 × 10⁶, and 439.83 × 10⁶ tons, respectively). The economy of Qinghai is mainly based on resource extraction and primary processing, leading to a large quantity of carbon emissions from high-energy-consuming industries. However, the economy in the eastern region is more developed, with a complete industrial system, higher proportion of manufacturing and high-tech industries, and large demand for resources. Therefore, these resources and the related products from Qinghai Province flow to the eastern region in large quantities. In this process, the carbon emissions from sectoral production activities are “indirectly” transferred to the eastern regions [59]. As shown in Figure 4b, the high carbon footprint of Qinghai Province is mainly from the northwest (2680.05 × 10⁶ t), followed by the east, southwest, northeast, north, central, and south (434.29 × 10⁶, 185.49 × 10⁶, 147.94 × 10⁶, 50.79 × 10⁶, 28.32 × 10⁶, and 24.92 × 10⁶ tons, respectively). Qinghai Province has close economic ties with northwestern regions, such as Xinjiang, Gansu, and Ningxia. Several basic raw materials and industrial products are supplied to Qinghai through these regions; these regions are relatively close and have lower transportation costs, forming a stable supply network. Furthermore, Qinghai Province has abundant resources, but its industrial foundation is relatively weak. Therefore, for high value-added products and technologies (including industrial equipment, consumer goods, and technology products), the region relies on external inputs from the east and other regions. These products generate a large quantity of carbon emissions during the production process.

3.3. Cross-Regional Spillover Impacts of PV Development on the Environment

As shown in Figure 5, S14 exhibited the highest electricity demand, followed by S24, S13, and S12. As shown in Figure 6, the electricity demands in the eastern regions of Jiangsu and Zhejiang, central regions of Henan, southern region of Guangdong, and southwestern regions of Chongqing and Sichuan were the highest in that year (5598.13 × 10⁸, 4037.18 × 10⁸, 3273.07 × 10⁸, 5732.14 × 10⁸, 933.81 × 10⁸, and 2025.6 × 10⁸ kWh, respectively). Jiangsu’s S14 (644.09 × 10⁸ kWh) and S24 (623.81 × 10⁸ kWh); Zhejiang’s S7 (389.74 × 10⁸ kWh), S13 (577.3 × 10⁸ kWh), and S24 (429.45 × 10⁸ kWh); Henan’s S14 (684.94 × 10⁸ kWh); Chongqing’s S14 (126.5 × 10⁸ kWh); Guangdong’s S24 (98.02 × 10⁸ kWh), S13 (612.78 × 10⁸ kWh), and S20 (488.43 × 10⁸ kWh); and Sichuan’s S24 (619.46 × 10⁸ kWh) and S14 (302.92 × 10⁸ kWh) were the sectors with the highest electricity demand. S24 represented the main sources for electricity supply; however, in the production process, they consumed large amounts of electricity and energy. For example, the driving systems of power generation equipment, such as boilers, steam turbines, cooling towers, fans, and pumps, as well as the stable operation of automation control and monitoring systems, require a large amount of electricity supply. Meanwhile, there are certain energy losses in the process of power transmission and conversion. Although these losses are technically inevitable, they also occur during the electricity consumption of the electricity production and supply sectors. In addition, the operation of auxiliary systems, such as cooling systems, fuel processing and transportation, and emission control, also require a large amount of electricity. Power plants adopt cogeneration systems, which produce heat (i.e., steam and hot water) while producing electricity. The operation of equipment during the heat production and transmission process (e.g., hot water pumps and steam pipelines) can also increase the power plant’s demand for electricity.

The national thermal power generation pattern and footprint flow of PV power generation products in Qinghai Province are shown in Figure 7. In 2021, Shandong, Inner Mongolia, Jiangsu, and Guangdong were the top thermal power producers in China (5281.04 × 10⁸, 4891.80 × 10⁸, 4841.31 × 10⁸, and 4638.57 × 10⁸ kWh, respectively). The carbon emissions generated during their power generation process caused substantial pressure on the regional eco-environment. Based on the footprint of PV products in Qinghai, we could conclude that the PV development in Qinghai strongly supports the demand in the central and eastern coastal areas, especially in the five provinces of Zhejiang, Guangdong, Sichuan, Jiangsu, and Henan. The corresponding supply of PV products in Qinghai has reached 104.52 × 10⁶, 91.13 × 10⁶, 86.60 × 10⁶, 78.37 × 10⁶, and 73.37 × 10⁶ RMB, respectively. The supply of clean energy in Qinghai has greatly alleviated the carbon emissions in these areas.

The cross-regional sectoral footprint of PV products in Qinghai is shown in Figure 8. The flow of PV products from Qinghai to various sectors in the remaining 30 provinces has made great contributions to the implementation of carbon reduction strategies. Specifically, the wholesale and retail industries in 30 provinces (S28) have a high dependence on PV products in Qinghai Province, while metal ore mining and selection products (S4) and non-metallic ore and other mineral mining and selection products (S5) provide raw material for the stable supply of PV products in Qinghai Province. Jiangsu, Zhejiang, Shandong, Henan, Hubei, Hunan, Guangdong, Sichuan, and Shaanxi all have high dependence on the PV products produced in Qinghai Province. The eastern and central regions have developed economies, high levels of industrialization, and high demand for electricity. However, these regions also face environmental pollution and energy security issues. Therefore, these regions actively adjust their energy structure; reduce their dependence on fossil fuels, such as coal; and alternatively use more clean energy. In China, policies such as the West–East Electricity Transmission have been implemented to encourage the development and utilization of renewable energy in the western region and transmit them to the eastern and central regions, supporting energy balance and structural adjustment nationwide [60]. The regional contribution of PV development in Qinghai Province in reducing carbon footprint is shown in Figure 9. PV development in Qinghai Province substantially reduced the carbon emissions in the eastern, southwestern, and central regions (by 625.20 × 10⁶, 407.19 × 10⁶, and 324.91 × 10⁶ tons, respectively). In the southern, northwestern, northern, and eastern regions, the emissions were reduced by 222.60 × 10⁶, 180.42 × 10⁶, 165.49 × 10⁶, and 92.36 × 10⁶ tons, respectively.

The southwestern region has abundant hydropower resources; however, it relies on thermal power during dry seasons and peak electricity consumption periods. The PV power produced in Qinghai Province is used to supplement the power supply during these periods, thereby reducing the use of thermal power and reducing the carbon emissions in these regions [61]. The eastern region has a developed economy and a high degree of industrialization and urbanization; therefore, it is the center of China’s manufacturing and service industries. Its main industries include electronics, automobiles, chemicals, textiles, and financial services. The southern region is dominated by light industry, manufacturing, and tourism, with rapid economic development and increasing electricity demand. The northern and northeastern regions are China’s old industrial bases, with the major industries being steel, machinery manufacturing, chemicals, and agriculture. These regions have huge electricity demands, and the PV energy produced in Qinghai is transmitted to these areas on a large scale to decrease the regions’ reliance on fossil fuels (such as coal) for power generation. Thus, PV development can reduce carbon emissions, improve environmental quality, and promote economic development, concurrently facilitating the achievement of sustainable development goals (SDGs) nationwide [62].

4. Discussion

4.1. Spillover Impact of PV Development on the Economy and Environment at a Cross-Regional Scale

Energy consumption directly promotes regional economic growth and improves industrial development, and fossil-fuel combustion ensures stable energy supply [63,64]. With the increasing consumption of fossil fuels, the intensity of environmental issues, such as air pollution and climate warming, is growing [65]. Frequent extreme weather and the chain reactions caused by the greenhouse effect have forced the world to reconsider reducing its dependence on fossil fuels [63]. Countries around the world are focusing on the development of clean energy, with solar energy being the most abundant, sustainable, widely used, and safest energy source [66,67]. Compared to fossil-fuel power generation, which entails the production of harmful gases, such as nitrogen oxides and sulfur oxides, PV power generation is a clean source (with no emissions), thereby reducing the risk of air pollution and acid rain and improving regional air quality [25,66]. Notably, the construction and operation of PV projects in Qinghai Province have introduced a large number of employment opportunities and economic benefits to the locals, promoting local economic development. This includes the implementation of large-scale PV projects, which have driven the development of local PV industry chain, from silicon material production to PV module manufacturing, system integration, operation, and maintenance services, forming a complete industrial chain and enhancing the overall economic competitiveness at the regional level [68,69]. Notably, PV development can promote the utilization of wasteland and other unused land resources, optimize the land use structure, improve the local eco-environment, and effectively prevent land desertification through ecosystem restoration measures (such as vegetation restoration and soil improvement) during the construction of phase, thereby enhancing the land productivity and supply capacity of ecosystem services and promoting a virtuous cycle of regional eco-environment [63,66,69].

The PV development in Qinghai Province has spillover impacts on the economy and environment at the cross-regional scale. Through the “West–East Power Transmission” policy and ultrahigh-voltage transmission technology, PV power produced in Qinghai Province can be transported to areas with high electricity demand (the eastern, central, and southern regions), to reducing their dependence on coal-fired power [70]. Jiangsu, Zhejiang, and Guangdong have developed economies and high levels of industrialization, with a huge demand for electricity. The input of PV energy produced in Qinghai Province has effectively replaced a part of the coal-fired power mix used in these regions, thereby reducing the carbon dioxide and air-pollutant emissions in these regions and improving environmental quality. Distant power transmission has also enabled the circulation of clean energy nationwide, promoting carbon reduction and energy structure transformation at a national scale. In addition, the electricity demand of other sectors in Qinghai Province is partially met by PV power generation during the production process, greatly reducing the carbon emissions generated by the consumption of thermal power during the production process. These products are transported to other provinces through cross-regional trade, indirectly reducing the generation of cross-regional carbon footprints, achieving green transformation, and improving the market competitiveness of export products produced in Qinghai Province. Through these measures, the PV development in Qinghai Province enables green development in the province, while contributing to the national energy structure and regional environmental protection. The cross-regional PV power transmission and trade cooperation in Qinghai demonstrates the enormous potential of clean energy for promoting sustainable development, further strengthening the economic ties and environmental cooperation between China’s regions, serving the country’s boarder aim of achieving its carbon reduction goals.

4.2. Roles and Challenges of PV Development in Achieving Carbon Neutrality

Photovoltaic (PV) development plays an important role for China in achieving carbon neutrality [63]. First, by replacing fossil-fuel power generation, PV power generation largely reduces carbon dioxide and other GHG emissions, increases the proportion of clean energy in the energy mix, and promotes energy structure optimization [63,65]. PV development in Qinghai Province is based on the utilization of the region’s abundant solar energy resources, to provide clean electricity for the entire country. Through the “West–East Electricity Transmission” project, the PV energy produced in Qinghai Province is transmitted to areas with high electricity demand, thereby reducing their dependence on fossil-fuel power and achieving carbon reduction. Second, the development of the PV industry has driven upgrades in related industrial chains and growth in the local economy. Furthermore, the construction and operation of PV projects have created a large number of employment opportunities, promoted local economic development, and improved locals’ income and living standards. Moreover, PV development has also promoted technological innovation, facilitated the progress of related technologies (such as photovoltaic cells, energy storage technology, and smart grids), and enhanced the overall technological level and industrial competitiveness in the region. In addition, PV power stations utilize wasteland and unused land resources, thereby optimizing land use, improving the local eco-environment (through vegetation restoration and ecological governance), and promoting ecological construction. The successful development of the PV industry in China demonstrates the power of international cooperation and technology output and enhances China’s influence in the global clean energy field.

However, PV development also poses several challenges in achieving carbon neutrality. First, PV power generation relies on sunlight, known to have intermittency and instability issues. Its power generation capacity is limited during nighttime or rainy weather. The large-scale integration of PVs into the power grid also puts higher demands on the scheduling and stability of the grid, requiring an improvement in the flexibility and adaptability of the grid. To solve these problems, large-scale energy storage technology and smart grid support are needed to ensure stable power supply. At present, energy storage technology is not mature, and further breakthroughs are needed with respect to battery life, efficiency, and environmental issues, which constrains the large-scale application of the technology [71]. Second, the construction of smart grids requires considerable investment and technical support to achieve efficient transmission and management of PV power. Moreover, PV projects require large initial investments and pose financing difficulties, coupled with high costs related to PV modules and energy storage equipment, which affects the overall economy of such projects. Governments and enterprises need to innovate financing mechanisms and policy support to reduce the financing costs of PV projects and attract more social capital investment [72]. In addition, policies and market mechanisms need to be improved to stimulate more investment and technological innovation. Although the government has introduced some policies to support the PV industry, several issues in the implementation process, such as incomplete policies and inadequate subsidies, persist [73,74]. Therefore, it is necessary to further improve the policy system and provide continuous policy support [73,74]. Notably, large-scale PV power plants require a large amount of land, which may conflict with agricultural land and ecological protection goals, and require reasonable planning and layout [16,49,75]. Furthermore, the construction of PV power plants may have certain impacts on the local eco-environment, e.g., the destruction of wildlife habitats; thus, effective environmental protection measures need to be implemented to address these issues [37,66]. Finally, it is necessary to establish a reasonable PV grid pricing mechanism and an effective carbon trading market to promote the sustainable development of PV power generation. Improved policy support, reasonable PV electricity prices, and effective market incentives are crucial for promoting the development of the PV industry [24]. The government should increase policy support for the PV industry, promote the continuous reduction in the associated costs, and enhance market competitiveness [24,63].

5. Conclusions

In this study, we systematically investigated the spillover impacts of PV development in Qinghai Province on the economy and environment at the cross-regional scale, using the EE-MRIO approach. The main findings are explained below:

• (1). From 1995 to 2021, traditional thermal power remained the main source of electricity in China. From 1995 to 2007, China’s thermal power generation accounted for an average of over 80% of the total electricity generation. Since 2008, the proportion of thermal power generation declined gradually, continuously promoting energy structure transformation. From 2015 to 2020, the number of PV power stations in China increased from 1637 to 10,947, with the largest increase being noted in the Qinghai and Ningxia provinces.

• (2). The cross-regional carbon footprint of Qinghai Province in 2021 affected the carbon emissions of Shandong, Hebei, Inner Mongolia, and Jiangsu provinces; the carbon footprint was the highest in these regions, and their carbon emissions were mainly from the production and supply sectors of electricity and heat (S24), followed by metal smelting and rolling processing products (S14), non-metallic mineral products (S13), and the transportation, warehousing, and postal sectors (S29). The carbon footprint of Qinghai Province mainly flowed to the eastern region, followed by the southwestern, central, southern, northwestern, northern, and northeastern regions.

• (3). In terms of the spillover effects of PV development in Qinghai Province, in 2021, the thermal power outputs of Shandong, Inner Mongolia, Jiangsu, and Guangdong were the highest in China. The PV development in Qinghai Province strongly supported the electricity demand in the central and eastern coastal areas, especially in the Zhejiang, Guangdong, Sichuan, Jiangsu, and Henan provinces. The PV development in Qinghai Province substantially reduced the carbon emissions in the eastern, southwestern, and central regions through the distant supply of PV energy.

In summary, by combining the data analysis and theoretical models, this study presents a comprehensive quantitative method to analyze the spillover effects of PV development in Qinghai Province while analyzing the role and challenges of PV development with respect to the country’s carbon neutrality strategy. Our study will serve as a valuable reference to policymakers for effective policy formulation related to sustainable energy supply.

Author Contributions

Conceptualization, C.J. and Y.W.; Methodology, Y.W.; Formal analysis, C.J. and Y.W.; Writing—original draft preparation, Z.Q. and C.J.; Writing—review and editing, C.J. and Y.Z.; Funding acquisition, C.J.; Resources, R.W.; Supervision, S.Y. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The authors confirm that the data supporting the findings of this study are available within the article.

Acknowledgments

There is no acknowledgement involved in this work.

Conflicts of Interest

Author Yixin Wang was employed by the company Yellow River Engineering Consulting Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Table

Figure 1. PV power generation and total power generation in 31 provinces, autonomous regions, and municipalities of China during 2015–2021.

Figure 2. Spatial patterns of (a-1,a-2) PV power generation and (b-1,b-2) total power generation in 2021 in 31 provinces, autonomous regions, and municipalities of China.

Figure 3. Carbon emissions in 31 provinces, autonomous regions, and municipalities of China in 2021 for the 42 sectors considered in this study. Note: S1—agricultural, forestry, animal husbandry, and fishery products and services; S2—coal mining and selection products; S3—oil and gas extraction products; S4—metal ore mining and selection products; S5—non-metallic minerals and other mineral mining products; S6—food and tobacco; S7—textile; S8—textile, clothing, shoes, hats, leather, and down and its products; S9—wood processing products and furniture; S10—paper printing and cultural, educational, and sports equipment; S11—petroleum, coking products, and nuclear fuel-processing products; S12—chemical products; S13—non-metallic mineral products; S14—metal smelting and rolling processed products; S15—metalware; S16—general equipment; S17—special equipment; S18—transportation equipment; S19—electrical machinery and equipment; S20—communication equipment, computers, and other electronic devices; S21—instruments and apparatuses; S22—other manufactured products; S23—repair services for metal products, machinery, and equipment; S24—production and supply of electricity and heat; S25—gas production and supply; S26—production and supply of water; S27—architecture; S28—wholesale and retail; S29—transportation, warehousing, and postal services; S30—accommodation and catering; S31—information transmission, software, and information technology services; S32—finance; S33—real estate; S34—leasing and business services; S35—scientific research; S36—technical service; S37—management of water resources, environment, and public facilities; S38—resident services, repairs, and other services; S39—education; S40—health and social work; S41—culture, sports, and entertainment; S42—public administration, social security, and social organizations [33,34].

Figure 4. Cross-regional carbon footprint: (a) outflow and (b) inflow of Qinghai Province in 2021.

Figure 4. Cross-regional carbon footprint: (a) outflow and (b) inflow of Qinghai Province in 2021.

Figure 5. Electricity demands in 2021 in China by sector and province.

Figure 6. Sectoral electricity consumption (unit: 108 kWh) for representative regions.

Figure 7. National thermal power generation and footprint flow of PV products of Qinghai Province in 2021.

Figure 8. Cross-regional and cross-sectoral carbon footprint of photovoltaic (PV) energy produced in Qinghai Province in 2021.

Figure 9. Contribution of PV development in Qinghai Province to the carbon footprint in 2021 at a cross-regional scale.

Table 1

Basic structure of the environmentally extended multi-regional input–output (EE-MRIO) model used in this study.

			Intermediate Demand			Final Demand			Total Output
			Region 1	…	Region m	Region 1	…	Region m
			Sector 1…Sector n	…	Sector 1…Sector n	Total Demand	…	Total Demand
Intermediate Input	Region 1	Sector 1	Z 11	…	Z 1m	Y 11	…	Y 1m	X 1
		…
		Sector n
	…	…
	Region m	Sector 1	Zm 1	…	Zmm	Ym 1	…	Ymm	Xm
		…
		Sector n
Added Value			V 1	…	Vm
Total Input			X 1	…	Xm
Environmental Satellite Account			E 1	…	Em

Note: Z_mm represents economic transaction data within each region and between different regional sectors. Y_mm represents the final consumption of goods and services in different regions, including the household consumption, government expenditure, capital formation, and exports. These data reflect the demand of end-users (including residents, governments, and consumers from other countries) in different regions. V_m denotes the non-intermediate commodity inputs required in the production process, e.g., labor, land use, and capital consumption, and the necessary basic resources and services in the production activity process. X_m denotes the total amount of products and services produced/consumed by an economic sector during a certain period of time, i.e., the total output/total input of the sector, reflecting the comprehensive level of a sector with respect to the regional economic activities. E_m is a key element of an EE-MRIO table; it characterizes the intensity of the impact of sectoral economic activities on the environment.

Appendix A

Figure A1. Timeseries of national power generation and proportion of thermal power generation from 1995 to 2021.

Figure A2. Evolution of power generation in 31 provinces, autonomous regions, and municipalities in China from 1995 to 2021.

Figure A3. Spatial distribution of photovoltaic (PV) power stations in China in 2015 (a) and 2020 (b), and their changes (c).

Figure A4. Carbon emissions in 2021 in China at the provincial level (unit: 106 tons).

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