Unveiling geographical layouts and synergies of large-scale wind and solar power deployment in China

Abstract

The spatiotemporal dynamics and synergistic relationship between wind and solar power deployment are crucial for China’s energy transition and carbon neutrality goals. Using spatial autocorrelation analysis and spatial econometrics modeling, this study examines the spatiotemporal characteristics of wind and solar power in China, investigates their synergistic relationship, and identifies the primary factors driving their deployment. The results reveal that the installed capacity of both wind and solar power in China increased substantially between 2014 and 2023, with spatial distributions exhibiting marked heterogeneity. Wind power was concentrated in North and East China, while solar power installations were primarily located in East and Northwest China. Compared with 2014, the distribution of wind and solar power installations became more dispersed by 2023, with spatial clustering weakening. At the regional level, solar installations, electricity consumption, and thermal power generation have significant positive effects on wind power installations, whereas resident population size and financial health status have negative effects. Furthermore, variables from neighboring provinces demonstrate notable spatial spillover effects, with wind and solar power deployment exhibiting strong synergies both within and across regions. This study provides comprehensive policy insights for realizing the Sustainable Development Goals and China’s dual objectives of carbon peaking and carbon neutrality, while supporting the global green transition and the advancement of a low-carbon economy.

Introduction

As environmental pollution from fossil fuel use intensifies, renewable energy technologies have become a focus of global attention. Wind and solar power technologies are considered not only as low-carbon alternatives to coal-fired power generation but also as the most promising renewable energy sources worldwide (Zhu, 2016). In the context of the global energy transition, the further deployment of wind and solar power has become an inevitable choice for accelerating the sustainable deployment of the renewable energy industry and achieving carbon peaking and carbon neutrality goals in China (Hong et al., 2020; Zhang et al., 2020).

China possesses abundant reserves of renewable energy, ranking first in its scale worldwide with the installed capacity of wind and solar power deployment constantly expanding (Li and Kong, 2019). The annual average wind power density in China is approximately 184.5 W/m², with wind energy resources mainly concentrated in the northwest, north, and northeast regions. In addition, the annual average solar radiation is 1490.8 kWh/m², with marked spatial heterogeneity arising from topographic variation, higher radiation is observed in Northwest China, while lower values prevail in the southeast (Yao et al., 2022; China Meteorological Administration, 2021). As of the end of 2020, the cumulative installed capacity of renewable energy power generation in China reached 934 million kW, reflecting a 17.5% increase from the previous year. Of this, wind power accounted for 281 million kW, primarily concentrated in Northwest, North, and Northeast China, while solar installed capacity amounted to 253 million kW, mainly distributed in Northwest and Northeast China.

Although both wind and solar power are characterized by inherent intermittency and volatility, their collaborative layout effectively mitigates these instability issues through the mechanism of multi-energy complementarity. This arises from the natural spatiotemporal asynchrony of the two resources, where the deficit of one source is offset by the surplus of the other, thereby smoothing the aggregate output curve and enhancing grid stability. Previous studies have confirmed that the deployment of wind and solar power involves complex synergistic, complementary, or competitive interactions (Chrifi-Alaoui et al., 2023; Duan et al., 2014), with complementarity being the key to overcoming intermittency. For instance, Ren et al. (2019) evaluated the spatial and temporal complementarity of wind and solar resources across China using MERRA-2 reanalysis data. Their results indicated that wind–solar complementarity performs significantly better than the interregional complementarity of a single energy type, underscoring the necessity of hybrid development and spatially diversified deployment. A similar conclusion was reached by Jerez et al. (2013) via the simulated annealing algorithm to identify optimal locations for solar and wind power plants across the Iberian Peninsula. Their findings demonstrated strong spatio-temporal complementarity, particularly at the monthly scale, proving that integrated siting strategies can effectively mitigate the inherent variability of single renewable resources. Furthermore, Lv et al. (2022) analyzed the spatiotemporal variation of wind and solar energy, identifying obvious spatial heterogeneity that forms the physical basis for such complementarity. de Souza Nascimento et al. (2022) further extended this to the marine environment, assessing the theoretical and technological potential of offshore wind and solar energy and demonstrating their significant annual and hourly complementarity in time.

To date, previous studies have mainly focused on the regional disparities and spatiotemporal changes in wind and solar power, as well as varying combinations of renewable energy (Li et al., 2023; Wang et al., 2022). However, few research studies have delved deeply into the competitive and cooperative relationship and the distinct driving mechanisms of wind and solar power deployment at the provincial level in China. With the rapid deployment of wind and solar power facilities, it is paramount to move beyond descriptive patterns and clarify how the two sectors interact and what drives their differences. This impetus paves the way for a sustainable expansion of these sectors and the achievement of carbon peaking and carbon neutrality goals. When compared, this integrated framework enables not only the identification of spatial dependencies but also the quantification of inter-provincial interactions. Moreover, while existing research often treats influencing factors indiscriminately, this study explicitly differentiates the drivers of wind and solar deployment while simultaneously assessing their coordination. Accordingly, the remainder of this article is structured as follows: Section Method and data sources introduces the spatial autocorrelation analysis, the Spatial Durbin Model, and the data sources; Section Results delineates the spatiotemporal distribution, synergistic relationships, and the main driving factors influencing deployment; and finally, Section Discussion and policy implications and Conclusions present the discussion and conclusions, encapsulating the key findings and policy implications.

Literature review

This section moves beyond simple capacity statistics to review three interconnected dimensions, that is, the synergistic and competitive dynamics of these technologies, their spatial distribution characteristics, and the socioeconomic factors driving their expansion.

Research progress on wind-solar collaborative layouts

Wind and solar power exhibit inherent complementarity across both spatial and temporal dimensions. More critically, their combined spatiotemporal synergy significantly enhances grid absorption capacity and transmission stability (Schindler et al., 2020). Compared to stand-alone configurations, hybrid deployment confers multidimensional benefits upon future power systems (Solomon et al., 2016). Specifically, leveraging the spatial complementarity between wind and solar resources effectively mitigates the necessity for large-scale energy storage infrastructure (Lugovoy et al., 2021). Empirical evidence consistently confirms that hybrid generation systems, by capitalizing on this resource interdependence, improve operational performance and secure energy supply across diverse geographical contexts (Jain et al., 2020; Muchiri et al., 2023). Extending this concept to broader system integration, Cui et al. (2024) analyzed the coupling of wind, PV, and hydropower, demonstrating how rational configuration unlocks complementary advantages to optimize system efficiency.

Conversely, the concurrent deployment of wind and solar power may also engender competitive dynamics. With the gradual phase-out of government subsidies, these sectors are increasingly compelled to demonstrate intrinsic economic viability, thereby intensifying the drive for technological innovation and industrial restructuring (Lv et al., 2020). Furthermore, Baker and Sovacool (2017) scrutinized the potential conflicts between wind and PV industries within South Africa and global production networks, highlighting how competitive barriers can impede the localization of renewable technologies. From an innovation diffusion perspective, while Dawson et al. (2000) utilized the technology adoption lifecycle model to delineate the promotion pathways of wind-PV synergistic technologies, such frameworks often lack a direct mechanistic link to the enhancement of overall energy system efficiency.

Research progress on spatial layouts of renewable energy deployment

To date, a substantial body of literature has scrutinized the spatial distribution, regional disparities, and spatiotemporal dynamics of renewable energy systems (Li et al., 2022; Xia et al., 2023). Within this domain, Quito et al. (2023) employed spatial econometric models to investigate energy efficiency correlations across European nations, demonstrating that renewable deployment generates positive spillover effects that enhance efficiency in neighboring regions. In the context of China, Xu et al. (2022) reported significant spatial agglomeration of renewable capacity, a trend particularly pronounced in the solar sector. Specifically, Shao and Fang (2021) utilized spatial autocorrelation analysis, identified high degrees of clustering in cumulative solar power capacity, with high-high clusters aggregated in Northwest and East China and low-low clusters in the South and Southwest. Similarly, while Han et al. (2022) observed a more dispersed pattern in newly added distributed solar power stations, Zhou et al. (2020) documented robust positive spatial autocorrelation and spillover effects for provincial wind power installations. The frameworks rooted in spatial economics, such as the theory of resource endowment differentials (Fujita et al., 2001) and spatial agglomeration (Krugman, 1991) offer valuable lenses for examining the spatial mismatch between renewable resource endowments and load centers, however, their specific application to the synergistic deployment of wind and solar technologies remains underexplored.

Research progress on driving factors of renewable energy layouts

Numerous studies have dissected the multifaceted drivers of renewable energy development, spanning social, economic, and technological dimensions (Gottschamer and Zhang, 2016; Omri and Saidi, 2022). Empirically, Li et al. (2022), drawing on generation data from 2000 to 2018, identified energy demand, technological progress, and demographic composition as critical determinants of deployment. Zhou and Li (2022) analyzed the Global Green Finance Index (1986–2019), elucidating a robust positive correlation between green finance mechanisms and the sustainable expansion of renewable energy. Seriño et al. (2022) investigated renewable diversification across 117 nations over three decades, revealing that high per capita income, robust policy frameworks, and technological innovation are pivotal for fostering diversification. Similarly, utilizing vector error correction models for the 1990–2020 period, Kilinc-Ata and Dolmatov (2023) pinpointed income levels, aggregate energy consumption, and carbon emissions as primary contributors to capacity expansion. Beyond market and technological forces, policy interventions exert a profound influence on the trajectory of renewable energy development. For instance, Su and Zhang (2015) conducted a comparative analysis of policy mixes in the United States, Germany, and China, concluding that the Chinese solar power industry necessitates a more balanced and targeted policy architecture to sustain its growth.

Method and data sources

This study employs spatial autocorrelation analysis to identify regional clustering patterns, followed by the specification of spatial econometric models to isolate the impact of resource endowment and socioeconomic factors, along with the description of the selected variables and data sources are detailed below.

Spatial autocorrelation analysis

This study employs spatial autocorrelation analysis to examine the spatiotemporal clustering characteristics of wind and solar power deployment across China’s provincial administrative regions. A binary adjacency matrix W is constructed to define spatial relationships, where the element w_ij is set to 1 if province i is adjacent to province j, and 0 otherwise. Based on this spatial weight matrix, we utilize both Global Moran’s I and Local Moran’s I (LISA) to quantify spatial dependencies.

The Global Moran’s I is calculated to test whether the installed capacity of renewable energy exhibits significant spatial agglomeration or differentiation across the entire study area. The index ranges from −1 to 1, where a value significantly greater than 0 indicates positive spatial correlation (clustering), a value less than 0 indicates negative correlation (dispersion), and a value near 0 implies a random distribution. To further identify localized spatial patterns that global statistics might overlook, we employ the Local Moran’s I. This metric assesses the degree of correlation between each province and its neighbors, allowing for the visualization of High-High (HH), Low-Low (LL), High-Low (HL), and Low-High (LH) through LISA cluster patterns.

Spatial econometric modeling

When variables exhibit spatial dependence, conventional estimation methods such as ordinary least squares (OLS) or generalized method of moments (GMM) may yield biased results. Spatial econometric models explicitly account for these dependencies. The three primary models are the Spatial Lag Model (SLM), the Spatial Error Model (SEM), and the Spatial Durbin Model (SDM).

SLM is appropriate when the dependent variable itself exhibits spatial dependence. SEM applies when residuals are spatially correlated. SDM extends SLM and SEM by incorporating spatial lags in both the dependent and independent variables, making it suitable for contexts in which interdependencies exist across multiple dimensions. When there is spatial dependence in both the dependent variable and the independent variables, the SDM should be selected.

The basic expression of the spatial error model is as follows:

$$Y_{it}={\alpha }_i+{\lambda }_t+\beta X_{it}+{\epsilon }_{it}$$

The basic expression of the spatial lag model is as follows:

$$Y_{it}={\alpha }_i+{\lambda }_t+\rho W{Y}_{it}+\beta X_{it}+{\epsilon }_{it}$$

The basic expression of the spatial Durbin model is as follows:

$$Y_{it}={\alpha }_i+{\lambda }_t+\rho W{Y}_{it}+\beta X_{it}+\theta W{X}_{it}+{\epsilon }_{it}$$

where, i represents the spatial unit, t represents the year, W represents the spatial weight matrix, $W{Y}_{it}$ and $W{X}_{it}$ are the spatial lag terms of the explained variable and the explanatory variables respectively; $\epsilon$ represents the random variable; ${\alpha }_i$ and ${\lambda }_t$ represent the individual and time fixed-effect coefficients respectively; $\rho$ , $\beta$ and $\theta$ represent the parameters.

Different factors usually influence the development and distribution of wind and solar power installations. In addition to the endowment of wind and solar energy resources, factors such as freight volume, electricity consumption, permanent population at the end of the year, thermal power generation, financial health status, and freight turnover volume also significantly affect the installed capacity of wind and solar power. In this study, the spatial econometric model is used to further identify the main driving factors affecting the installation of wind and solar power. The specific expression of the spatial Durbin model for studying the influencing factors of wind and solar power installations is as follows:

$$\begin{array}{l}{LnWind}_{\mathrm{it}}={\alpha }_{\mathrm{i}}+{\lambda }_{\mathrm{t}}+{\rho }_0{\mathrm{W}LnWind}_{\mathrm{it}}+{\beta }_{1}LnPV\mathrm{it}+{\beta }_2{LnFreight}_{\mathrm{it}}+{\beta }_3{LnTEC}_{\mathrm{it}}\\ +{\beta }_4{LnPOP}_{\mathrm{it}}+{\beta }_5{LnThermal}_{\mathrm{it}}+{\beta }_6{LnThermal}_{\mathrm{it}}{\theta }_1{LnPV}_{\mathrm{it}}+{\theta }_2{LnCO}_{2\mathrm{it}}\\ +{\theta }_3{LnWS}_{\mathrm{it}}+{\theta }_4{LnHighway}_{\mathrm{it}}+{\theta }_5{LnPOP}_{\mathrm{it}}+{\theta }_6{LnFinance}_{\mathrm{it}}+{\epsilon }_{\mathrm{it}}\end{array}$$

$$\begin{array}{l}{LnPV}_{\mathrm{it}}={\alpha }_{\mathrm{i}}+{\lambda }_{\mathrm{t}}+{\rho }_0{\mathrm{W}LnPV}_{\mathrm{it}}+{\beta }_1{LnWind}_{\mathrm{it}}+{\beta }_2{LnFreight}_{\mathrm{it}}+{\beta }_3{LnTEC}_{\mathrm{it}}\\ +{\beta }_4{LnPOP}_{\mathrm{it}}+{\beta }_5{LnThermal}_{\mathrm{it}}+{\beta }_6{LnThermal}_{\mathrm{it}}{\theta }_1{LnPV}_{\mathrm{it}}+{\theta }_2{LnCO2}_{\mathrm{it}}\\ +{\theta }_3{LnWS}_{\mathrm{it}}+{\theta }_4{LnHighway}_{\mathrm{it}}+{\theta }_5{LnPOP}_{\mathrm{it}}+{\theta }_6{LnFTK}_{\mathrm{it}}+{\epsilon }_{\mathrm{it}}\end{array}$$

Data sources

The dependent variables are the installed capacities of wind and solar power across provincial-level administrative regions from 2014 to 2023, obtained from the China Electric Power Statistical Yearbook. To examine the synergies between the two technologies, wind and solar capacities are treated as explanatory variables for each other. In addition, a set of socioeconomic and energy-related variables is incorporated to capture the broader drivers of renewable deployment. The selection of freight volume, electricity consumption, permanent population at year-end, thermal power generation, financial health status, and freight turnover volume is grounded in the systematic logic of regional energy supply–demand balance, economic development, and institutional support for renewable energy (see Table 1).

Table 1.

Indicator list and data sources.

Variable	Indicator	Unit	Data source
LnPV	Photovoltaic installed capacity	10 MW	China Electric Power Statistical Yearbook
LnWind	Wind power installed capacity	10 MW	China Electric Power Statistical Yearbook
LnFreight	Freight volume	100 million ton-km	National Bureau of Statistics of China
LnTEC	Electricity consumption	GWh	China Electric Power Statistical Yearbook
LnPOP	Permanent population at the end of the year	10,000 people	National Bureau of Statistics of China
LnThermal	Thermal power generation	100 million kWh	National Bureau of Statistics of China
LnFinance	Financial health status	%	National Bureau of Statistics of China
LnFTK	Freight turnover volume	100 million ton-kilometers	National Bureau of Statistics of China

Note: The financial health status is the ratio of local general budget revenue to local general budget expenditure.

Freight volume is chosen as it reflects the logistical support for wind and solar infrastructure deployment, with empirical data showing a strong correlation with economic growth (Chu, 2021). Electricity consumption is included due to its established causal link to economic growth, supported by co-integration analyses indicating short- and long-term effects (Lin and Liu, 2016). The permanent population at the end of the year is selected as population decline impacts energy demand and planning for renewable projects (Yang and Qi, 2024). Thermal power generation is considered for its role in the current energy mix, influencing the transition to renewables as coal prices fluctuate (Lin and Shi, 2024). Financial health status is picked for its role in funding renewable energy through fiscal policies, which significantly affect economic growth (Kim et al., 2021). Freight turnover volume is incorporated due to its positive correlation with GDP, highlighting transportation efficiency for renewable energy logistics (Tong and Yu, 2018). The chosen variables and data sources are listed in Table 1 for reference.

Results

This section details the empirical analysis of the spatiotemporal evolution characterizing China’s renewable energy sector, delineating the distributional patterns of wind and solar power installations to identify regional heterogeneities, and verify the significance of geographical clustering, the specific driving mechanisms and spillover effects governing the industrial layout.

Spatial and temporal energy characteristics

Figure 1 presents the spatiotemporal distribution and development trends of wind and solar power installations in China. With respect to wind power, China’s cumulative installed capacity expanded rapidly from 9636.34 MW in 2014 to 44,116 MW in 2023. Spatially, wind installations are concentrated mainly in North and East China, while South China and western regions remain relatively limited. In 2014, Inner Mongolia and Xinjiang recorded installed capacities of 6,961 MW and 3258 MW, respectively. Notably, Henan and Guangxi experienced the most pronounced growth, with increases of 4869.19% and 10,076.71%, ultimately reaching 2178 MW and 1267 MW in 2023.

Figure 1.

Wind and solar power installed capacities. Note: red represents the solar installed capacities, and blue represents the wind installed capacities: (a) 2014 and (b) 2023.

For photovoltaic power, total installed capacity rose from 2792 MW in 2014 to 60,635.3 MW in 2023. Spatially, East China and Northwest China have the highest levels of solar installations, whereas Southwest and South China have comparatively lower levels. Shandong and Hebei recorded the largest increases, with capacity expansions of 5632.5 MW and 5266.4 MW, respectively. Henan, Jiangsu, Zhejiang, and Anhui also exhibited substantial growth, each exceeding 3000 MW during the same period. Generally, most provinces in China achieved positive growth in wind and solar installations, with notable breakthroughs observed in regions such as Guizhou and Chongqing.

Spatial autocorrelation analysis

This study employs a spatial contiguity weight matrix to compute the global Moran’s I, with the results presented in Table 2. The findings indicate that, except for solar installations in 2021 (p-value significant at the 5% level), all other p-values for wind and solar installations are significant at the 1% level, and the Moran’s I values are consistently positive. These results confirm a statistically significant positive spatial autocorrelation in China’s wind and solar installations, suggesting pronounced spatial clustering across provinces. Accordingly, the application of spatial econometric models is methodologically well-founded.

Table 2.

Global Moran’s I value of China’s wind and solar power installation.

Variable	Wind			Solar
	I	z	p-value	I	z	p-value
2014	0.146	3.171	0.002	0.132	2.911	0.004
2015	0.147	3.189	0.001	0.138	3.026	0.002
2016	0.135	2.970	0.003	0.172	3.618	0.002
2017	0.130	2.889	0.004	0.161	3.428	0.001
2018	0.131	2.907	0.004	0.163	3.461	0.005
2019	0.143	3.110	0.002	0.152	3.274	0.001
2020	0.126	2.813	0.005	0.126	2.820	0.005
2021	0.111	2.552	0.016	0.120	2.725	0.064
2022	0.135	2.968	0.003	0.105	2.449	0.014
2023	0.094	2.245	0.025	0.083	2.055	0.040

To visually examine the local spatial heterogeneity and evolution of renewable energy deployment, Moran’s I scatter plots for wind and solar installed capacity were generated for the years 2014 and 2023 (Figure 2). The scatter points are predominantly distributed within the first (High-High) and third (Low-Low) quadrants, indicating a persistent positive spatial correlation where provinces with similar installation scales tend to be geographically adjacent. However, a longitudinal comparison reveals a notable weakening in this spatial agglomeration; the Moran’s I index for solar power declined from 0.1316 in 2014 to 0.0828 in 2023, while the index for wind power decreased from 0.1465 to 0.0936 over the same period. This attenuation in spatial dependence implies that, while regional clustering remains a defining feature, the spatial layout of wind and solar power in China is gradually shifting from highly concentrated hotspots toward a more balanced, diffuse deployment pattern across provinces.

Figure 2.

Moran scatter plot of wind and solar power deployment installations in 2014 and 2023.

Driving mechanism identification

Given data heterogeneity, multiple model choices were considered. To select an appropriate spatial econometric model, LR tests, Wald tests, and Hausman tests were conducted for wind power and solar data. For wind power, LR tests were significant at the 1% level, indicating that the spatial Durbin model (SDM) does not degenerate into the spatial lag or error models, making SDM more suitable. Wald tests, also significant at 1%, further confirmed this suitability. Hausman tests were significant at 1%, supporting the use of a fixed effects model. In the LR time-space effect test, time effects were significant at 1%, whereas spatial effects were not significant at 10%, suggesting that a time-fixed SDM is appropriate. For solar, LR and Wald tests were significant at 1%, indicating SDM’s suitability. Hausman tests were significant at 1%, supporting a fixed effects specification. To ensure consistency and to examine mutual influences between wind and solar, the same time-fixed SDM was applied for both (Table 3).

Table 3.

Results of spatial econometric model selection tests.

Type		Wind	Solar
LR test	LR-SDM/SEM	29.76*** (0.000)	20.58*** (0.002)
	LR-SDM/SAR	27.42*** (0.000)	22.00*** (0.001)
Wald test	Wald-SDM/SEM	23.41*** (0.000)	108.40*** (0.000)
	Wald-SDM/SAR	28.66*** (0.000)	55.20*** (0.000)
Huasman test	Huasman	28.49*** (0.007)	35.97*** (0.000)
LR test	LR-both/time	247.17*** (0.000)	245.91*** (0.000)

***

, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. Values in parentheses indicate p-values.

Spatial Durbin model fitting results

Based on the test results, the SDM was fitted using Stata 18.0. In Table 4, Main represents effects of explanatory variables in a given region on local dependent variables, while Wx captures effects from other regions, reflecting spatial spillovers. ρ is the spatial autoregressive coefficient, with its significance indicating the presence of spatial effects. The detailed fitting results are presented in Table 4.

Table 4.

Spatial Dubin model estimation results.

Wind			Solar
	SDM			SDM
variable	Main	Wx	variable	Main	Wx
LnPV	0.107*** (0.007)	0.255*** (0.003)	LnWind	0.368*** (0.000)	1.221*** (0.000)
LnFreight	0.249** (0.012)	0.837*** (0.003)	LnFreight	−0.113 (0.495)	−0.022 (0.961)
LnTEC	1.378*** (0.000)	2.185*** (0.000)	LnTEC	1.375*** (0.000)	−0.360 (0.504)
LnPOP	−0.601*** (0.000)	−0.523* (0.060)	LnPOP	−1.314*** (0.000)	−1.134*** (0.001)
LnThermal	0.325*** (0.000)	−1.214*** (0.000)	LnThermal	0.372*** (0.002)	0.256 (0.485)
LnFinance	−1.789*** (0.000)	−2.639*** (0.000)	LnFTK	0.105 (0.352)	0.166 (0.611)
ρ	−0.659** (0.000)		ρ	−0.247*** (0.007)
N	300		N	300
R 2	0.814		R 2	0.778

***

, **, * denote significance at the 1%, 5%, and 10% levels, respectively. Values in parentheses indicate p-values.

Based on the fitting results, regarding the factors influencing wind power installed capacity, the coefficients for solar installed capacity, electricity consumption, and thermal power generation in the Main column are 0.107, 1.378, and 0.325, respectively, all significant at 1%. This indicates that an increase in solar installed capacity, electricity consumption, and thermal power generation in the region positively promotes the growth of wind power installed capacity. This is mainly because solar as a renewable energy generation facility exhibits a synergistic deployment effect with wind power. Rising electricity demand stimulates the expansion of supply, while the environmental costs associated with thermal power generation further incentivize the growth of renewable energy installed capacity. Freight volume (0.249, 5%) promotes wind capacity by improving transport for wind power equipment. Population (−0.601) and financial health status (−1.789) are negative and significant, implying wind installations favor sparsely populated areas and reduced fiscal support suppresses growth. An improved financial health status implies reduced fiscal expenditure relative to income, leading to decreased fiscal support, which suppresses wind power installed capacity growth.

In the Wx column, the coefficients for solar installed capacity, freight volume, and electricity consumption are 0.255, 0.837, and 2.185, respectively, all significant at the 1% level, indicating that increases in these factors in neighboring regions substantially promote wind power installed capacity in the focal region. This reflects the synergistic effect of solar deployment in adjacent areas, the broader improvements in freight capacity facilitating the transport of wind power equipment, and the heightened electricity demand in neighboring regions stimulating regional wind power deployment. Conversely, the coefficients for thermal power generation and financial health status are −1.214 and −2.639, respectively, both significant at the 1% level and negative, suggesting that increased thermal power generation and reduced fiscal expenditure in neighboring regions suppress wind power installed capacity in the focal region.

Regarding the explanatory variables for solar installed capacity, the coefficients for wind power installed capacity, electricity consumption, financial health status, and freight turnover in the Main column are 0.289, 0.789, 1.018, and 1.252, respectively, all significant at the 1% level. This implies that increases in wind power installed capacity, freight turnover, electricity consumption, and improved fiscal conditions are key drivers of solar installed capacity growth. The coefficient for year-end resident population is −1.279, significant at the 1% level, indicating that population growth in the region significantly suppresses solar installed capacity expansion. The coefficient for thermal power generation is −0.184 and not significant at the 10% level, suggesting that increased thermal power capacity in the region does not have a meaningful suppressing effect on solar deployment.

In the Wx column, the coefficients for electricity consumption and year-end resident population are −5.378 and −7.072, respectively, both significant at the 1% level, indicating that population growth and increased electricity demand in neighboring regions significantly inhibit solar installed capacity in the focal region. The coefficients for wind power installed capacity, financial health status, and freight turnover are 0.611, 1.582, and 0.693, respectively, and not significant at the 10% level, suggesting that increases in these factors in neighboring regions do not exert a significant promoting effect on solar installed capacity. The coefficient for thermal power generation is −0.607, not significant at the 10% level, indicating that an increase in thermal power generation in adjacent regions does not significantly affect solar deployment.

In terms of the mutual influence between wind power and solar installed capacities, both benefit from shared infrastructure, land resources, coordinated policy promotion, and the application of energy storage and smart grid technologies, resulting in a mutually reinforcing phenomenon where each is a key factor in the other’s deployment. Similarly, in neighboring regions, due to resource allocation and regional coordination, the synergistic effect between wind power and solar deployment is strong.

Decomposition of spatial effects

Based on the SDM, the impacts of explanatory variables on the dependent variable can be decomposed into direct, indirect, and total effects. The direct effect reflects the impact of explanatory variables within the focal region on the dependent variable in the same region. The indirect effect, also referred to as the spillover effect, captures the influence of explanatory variables in neighboring regions on the dependent variable in the focal region. The total effect represents the combined influence of direct and indirect effects, indicating the overall impact of a change in an explanatory variable across all regions. The decomposition results are presented in Table 5.

Table 5.

Decomposition of spatial effects.

Wind				Solar
Variable	Direct effect	Indirect effect	Total effect	Variable	Direct effect	Indirect effect	Total effect
LnPV	0.086** (0.045)	0.133** (0.044)	0.219*** (0.000)	LnWind	0.318*** (0.000)	0.964*** (0.000)	1.282*** (0.000)
LnFreight	0.159 (0.128)	0.492** (0.014)	0.652*** (0.001)	LnFreight	−0.125 (0.456)	−0.000 (0.999)	−0.123 (0.757)
LnTEC	1.226*** (0.000)	0.949*** (0.000)	2.175 (0.000)	LnTEC	1.421*** (0.000)	−0.575 (0.161)	0.847 (0.100)
LnPOP	−0.589*** (0.000)	−0.091 (0.586)	−0.679*** (0.001)	LnPOP	−1.281*** (0.000)	−0.681** (0.014)	−1.962*** (0.000)
LnThermal	0.504*** (0.000)	−1.051 (0.000)	−0.548*** (0.003)	LnThermal	0.362*** (0.001)	0.129 (0.684)	0.490 (0.169)
LnFinance	−1.584*** (0.000)	−1.089*** (0.000)	−2.673*** (0.000)	LnFTK	0.104 (0.347)	0.122 (0.653)	0.225 (0.494)

***

, **, * denote significance at the 1%, 5%, and 10% levels, respectively. Values in parentheses indicate p-values.

For wind power, the direct effects indicate that solar installed capacity (0.086**), electricity consumption (1.226***), and thermal power generation (0.504***) significantly promote regional wind power deployment. The coefficients for year-end resident population and financial health status are −0.589 and −1.584, respectively, both significant at the 1% level, suggesting that an increase in population and a decrease in fiscal expenditure relative to income significantly suppress wind power industry development. The coefficient for freight volume is 0.159 and not significant at the 10% level, indicating that improved freight capacity does not have a significant promoting effect on the wind power industry. The indirect effects show that solar installed capacity (0.133**), freight volume (0.492**), and electricity consumption (0.949***) in neighboring regions significantly promote wind power in the focal region, whereas thermal power generation (−1.051***) and financial health status (-1.089***) exert significant negative spillovers. Year-end resident population (−0.091) is not significant. The total effects for wind power demonstrate that solar installed capacity (0.219***), freight volume (0.652***), and electricity consumption (2.175***) significantly promote wind power across all regions, while year-end resident population (−0.679***), thermal power generation (−0.548***), and financial health status (−2.673***) suppress it.

For solar, the direct effects indicate that wind power installed capacity (0.318***), electricity consumption (1.421***), and thermal power generation (0.362***) significantly promote local solar deployment. Year-end resident population (−1.281***) significantly suppresses it, while freight volume (−0.125) and freight turnover (0.104) are not significant. The indirect effects show that solar installed capacity (0.964***) in neighboring regions promotes solar deployment locally, whereas year-end resident population (−0.681**) in neighboring regions significantly inhibits it. Other factors, including freight volume, electricity consumption, thermal power generation, and freight turnover, are not significant. The total effects for solar indicate a strong positive mutual promotion effect from wind power installed capacity (1.282***), while year-end resident population (−1.962***) significantly suppresses solar deployment across all regions. Other variables do not have significant total effects.

Lag robustness test

To account for potential path dependency and the lagged influence between wind and solar power capacity, we conducted a robustness test. We introduced a one-period lag for both dependent and independent variables and re-estimated the models using a time-fixed Spatial Durbin Model. The results are presented in Table 6.

Table 6.

Lag robustness test.

Wind		Solar
Variable	Lagged by one period	Variable	Lagged by one period
LlnWind	0.859*** (0.000)	LlnPV	0.837*** (0.000)
LlnPV	0.037*** (0.000)	LlnWind	0.272 (0.276)
ρ	−0.363** (0.061)	ρ	−0.362* (0.063)
N	240	N	240
R 2	0.879	R 2	0.8704

***

, **, * denote significance at the 1%, 5%, and 10% levels, respectively. Values in parentheses indicate p-values.

Based on the lagged model results, for the wind power model (Dependent variable: lnWind), the coefficient of its own first-period lag (L.lnWind) is 0.859* and is significant at the 1% level. This indicates a strong path dependency, where wind power capacity from the previous year significantly and positively influences the current year’s capacity. The coefficient for the first-period lag of solar power (L.lnPV) is 0.037*. Although statistically significant, its magnitude is very small, suggesting that the previous year’s solar deployment has a limited positive effect on current wind power deployment.

For the solar power model (Dependent variable: lnPV), the coefficient of its own first-period lag (L.lnPV) is 0.837* and highly significant, also confirming strong path dependency. However, the coefficient for the first-period lag of wind power (L.lnWind) is 0.272 with a p-value of 0.276, rendering it statistically insignificant. This finding suggests that, within this model, the wind power capacity of the previous year does not have a demonstrable impact on the deployment of solar power in the current year. Finally, the spatial autocorrelation coefficients (ρ) remain negative and significant in both models (Wind: −0.363**, Solar: −0.362*), and their values are highly consistent with each other. This consistency further confirms the robustness of our model’s spatial specification.

Robustness tests

To further verify the robustness of the model, some explanatory variables were replaced. Specifically, for both wind and solar installed capacities, the year-end resident population (in 10,000) was replaced with the urban population (in 10,000). Following this substitution, the spatial Durbin model was re-estimated, with the results presented in Table 7. The robustness test results indicate that the spatial autocorrelation coefficient, as well as the coefficients and significance levels of explanatory variables for wind power installed capacity, exhibit minimal changes compared to the original model. This confirms the reliability of the initial conclusions and further validates the robustness of the spatial Durbin model.

Table 7.

Robustness test of substituted explanatory variables.

Wind			Solar
	Main	Wx		Main	Wx
LnPV	0.096** (0.017)	0.213 (0.017)	LnWind	0.445*** (0.000)	1.220*** (0.000)
LnFreight	0.219** (0.023)	0.886*** (0.001)	LnFreight	−0.189 (0.227)	−1.192*** (0.004)
LnTEC	1.408*** (0.000)	2.332*** (0.000)	LnTEC	0.833*** (0.001)	−1.559** (0.029)
LnPOP	−0.635*** (0.000)	−0.636** (0.028)	LnPOP	−0.585*** (0.002)	−2.143*** (0.001)
LnThermal	0.316*** (0.001)	−1.299*** (0.000)	LnThermal	0.669*** (0.000)	1.131*** (0.003)
LnFinance	−0.159*** (0.000)	−2.388*** (0.000)	LnFTK	0.380*** (0.001)	1.179 (0.000)
ρ	−0.663*** (0.000)		ρ	−0.275*** (0.002)
N	300		N	300
R 2	0.821		R 2	0.741

***

, **, * denote significance at the 1%, 5%, and 10% levels, respectively. Values in parentheses indicate p-values.

Discussion and policy implications

To contextualize the empirical findings within the broader framework of China’s energy transition, this study interprets the underlying dynamics shaping wind and solar power deployment, ositioned relative to existing literature, followed by targeted policy recommendations designed to optimize future industrial layout.

Spatial characteristics and driving mechanisms

Wind and solar power play a pivotal role in China’s low-carbon energy transition. However, with the rapid advancement of the energy transition process, the pace of wind and solar power deployment varies considerably across provinces. To further implement China’s carbon peaking and carbon neutrality targets and to promote the sustainable deployment of wind and solar power nationwide, this study integrated provincial data on freight volume, electricity consumption, year-end resident population, thermal power generation, financial health status, and freight turnover volume from 2014 to 2023.

From 2014 to 2023, China’s wind and solar power industries achieved rapid large-scale growth, with capacities rising from 9636.34 MW to 44,116 MW and from 2792 MW to 60,635.3 MW, respectively. All provinces recorded positive growth, with notable breakthroughs in regions such as Guizhou and Chongqing. Spatially, wind installations were concentrated in North and East China, while solar power deployment was more prominent in East and Northwest China. Spatial autocorrelation analysis revealed significant positive clustering effects at the provincial level, though the degree of clustering weakened over time. This suggests narrowing interprovincial disparities in renewable energy deployment, indicating an emerging trend toward more balanced regional development

From the perspective of driving mechanisms, both commonalities and differences are observed in the factors influencing regional wind and solar deployment. In terms of commonalities, regional electricity consumption exerts a significant positive effect on both wind and solar installed capacity, underscoring the central role of energy demand in driving renewable energy deployment. Conversely, year-end resident population exerts a significant negative effect on both, reflecting the empirical reality that renewable projects are more likely to be located in areas with lower population density.

In terms of differences, freight volume demonstrates a more pronounced positive effect on wind deployment but no significant effect on solar power, consistent with the greater transportation requirements of wind equipment. Meanwhile, financial health status exerts a positive influence on solar deployment but a negative effect on wind deployment, highlighting their differentiated reliance on fiscal support. Furthermore, wind and solar installations exhibit significant synergies, reinforcing one another’s development trajectories.

Spatial spillover effects from neighboring regions also exhibit marked heterogeneity. For wind power, solar installed capacity, freight volume, and electricity consumption in adjacent regions generate significant positive spillover effects, whereas thermal power generation and financial health status exert significant negative effects. In the case of solar deployment, neighboring wind installed capacity has a significant positive spillover effect, while the year-end resident population shows a significant negative impact; other spillover factors prove largely insignificant. Overall, wind power deployment is shaped by a wider range of interregional spillover factors, whereas those influencing solar deployment are relatively limited.

Robustness checks further confirm the reliability of these conclusions. Whether accounting for lagged effects of explanatory variables or substituting alternative core variables, the model consistently produces stable results, with no substantive changes in the direction or significance of key influencing factors. This indicates that the spatial econometric findings on wind and solar power deployment are both stable and credible.

Comparison between this study and existing studies

These results align with other studies demonstrating that economic development levels and resource endowments significantly promote renewable energy growth, including wind and solar power (Xu et al., 2022; Yu et al., 2021). Regarding spillover effects, technological innovation and regional carbon emissions have also been tested to stimulate the deployment of renewable energy in neighboring provinces (Han et al., 2022; Xiong et al., 2020). Additionally, policy interventions play a crucial role in shaping the deployment of renewable energy. Effective deployment strategies should be adapted to local conditions and supported through appropriate policy and regulatory frameworks (Nielsen et al., 2023).

Since the onset of wind and solar power deployment in China, the interaction between the two has been a focus of debate. Spatial correlation analysis in this study shows positive associations between wind and solar capacities and their respective lag terms, implying synergy in their deployment. Similar conclusions have been drawn by previous studies (Xu et al., 2022), which suggest that wind and solar can complement each other under certain conditions, thereby enhancing renewable utilization efficiency. However, other research highlights competition between wind and solar deployment (Giarola et al., 2021; Lv et al., 2022), mainly due to resource allocation, investment, and policy constraints. These findings suggest the need to consider regional heterogeneity and resource distribution when formulating policies and planning frameworks to foster synergy rather than competition (Table 8).

Table 8.

Comparison between this study and previous studies.

	Xu et al (2022)	Yu et al (2021)	Han et al (2022)	Xiong et al (2020)	Nielsen et al (2023)	This study
Period	2001–2022	2008–2018.	2012–2020	1992–2014	/	2014–2023
Subject	Renewable energy production	Hydropower, solar power, and wind power	Photovoltaic power	Energy-related carbon emissions	Renewable energy transition	Wind and photovoltaic power installations
Method	SDM	Stochastic frontier analysis, geographical detector model.	SDM	LMDI	GIS-based analyses, survey-based stakeholder consultation, grid analyses.	SDM
Conclusion	China’s renewable energy production shows prominent spatial agglomeration, significant positive spatial autocorrelation, apparent spillover effects.	Production inefficiency exists in hydropower, solar and wind power; key factors vary by energy type with synergistic effects, supporting policy and global extension.	China’s solar installation grew fast with spatial autocorrelation, positive drivers like power consumption and R&D, notable emission reduction benefits.	Economic output promotes and energy intensity suppresses such emissions most and offers relevant policy implications.	Transmission grid limits energy transition-related development, requiring permission to consider national targets and better administrative coordination.	China’s wind and photovoltaic power installations have grown rapidly, with heterogeneous spatial distribution and strong synergistic effects.

Policy strategies and implications

China has set a target of increasing the share of non-fossil energy consumption to approximately 25% by 2030, with total wind and solar installed capacity reaching 1.2 billion kW. Many provinces have released provincial renewable energy development plans to support national low-carbon goals. For example, Zhejiang was the first to issue a provincial plan, followed by Shandong, Shanxi, Qinghai, and Xinjiang, each of which proposed region-specific deployment strategies. Based on the findings of this study, several policy implications can be drawn as below:

(1) The deployment of wind and solar power is suggested to be aligned with local resource endowments. Regions rich in wind resources, such as Inner Mongolia and Xinjiang, are suitable for large-scale centralized wind projects, while decentralized projects may be more appropriate for resource-limited areas such as Chongqing and Guizhou. Similarly, large-scale solar projects should be concentrated in solar-abundant regions such as Qinghai and Shandong, while distributed solar may be more suitable for lower-radiation provinces like Sichuan and Guizhou. Such differentiated strategies can optimize resource use and ensure balanced industry expansion.

(2) To promote the sustainable deployment of renewables, factors such as technological innovation and financial investment need be fully leveraged. This includes advancing green patents, increasing R&D spending on wind and solar power, modernizing aging infrastructure, and promoting digitalization. Carbon emission regulations should also be strengthened, as they not only incentivize emission reductions but also enhance the competitiveness of renewables, creating a favorable environment for industry growth.

(3) To fully harness the abundant resources in Northwest and parts of North China, newly built large-scale wind–solar bases need to maintain a wind-to-solar capacity ratio between 1:0.8 and 1:1.2. This ratio maximizes temporal and spatial complementarity, stabilizing output and supporting grid balance. Moreover, storage capacity should reach at least 10% of total installed capacity to mitigate intermittency and store surplus generation. Establishing provincial-level joint forecasting systems for wind and solar output would improve predictive accuracy and scheduling. Priority dispatch in cross-regional transmission corridors is also necessary to facilitate efficient energy transfer and maximize the utilization of renewable energy sources.

Finally, it is worth noting that the primary limitation of this study lies in the use of provincial-scale data, which, although practical, may be overly aggregated. Provincial data were chosen due to their completeness and availability, as city- and county-level datasets contained extensive missing values. Ensuring data integrity at the provincial level was therefore crucial for ensuring the reliability of results. Future research will benefit from improving the accessibility of sub-provincial data, enabling more refined spatial analysis and deeper insights into renewable energy deployment dynamics.

Conclusions

Using spatial autocorrelation analysis and the Spatial Durbin Model (SDM), this study investigates the spatiotemporal distribution of wind and solar power deployment in China, analyzes their spatial autocorrelation patterns, identifies key driving factors, and explores the interrelationship between their spatial layouts. Overall, China has witnessed rapid growth in wind and solar installed capacity, with a significant positive correlation between the two. However, the driving factors of wind and solar power differ. Rising carbon emission concentrations, higher wind speeds, and improved transportation infrastructure promote wind power deployment, whereas increasing electricity consumption, stronger fiscal capacity, and enhanced freight transportation conditions foster solar power deployment. By revealing the spatiotemporal distribution characteristics of wind and solar power, clarifying their synergistic relationships across provincial regions, and assessing the influence of socio-economic factors, this study provides quantitative evidence and policy guidance to support the sustainable development of China’s wind and solar power industry.