Photovoltaic power potential of variability and its influencing factors in Xinjiang, China

doi:10.1016/j.jaridl.2026.07.005

Journal of Arid Land

2026, Vol. 18

Issue (7): 1258-1282 DOI: 10.1016/j.jaridl.2026.07.005

Research article

Photovoltaic power potential of variability and its influencing factors in Xinjiang, China

MENG Nana, LI Ran, PENG Yimo, GE Tianxu, FAN Shichen, ZHANG Haiwei^*(

)

College of Geography and Remote Sensing Sciences, Xinjiang University, Urumqi 830046, China

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Abstract

Quantifying the evolution of solar photovoltaic power potential in Xinjiang Uygur Autonomous Region (hereinafter referred as to Xinjiang) is critical for energy planning under China's carbon neutrality goals. This study applied the Equidistant Cumulative Distribution Function Matching (EDCDF_m) method to correct biases in the sixth phase of the Coupled Model Intercomparison Project (CMIP6) multi-model data, using the fifth generation of European Centre for Medium Range Weather Forecasts (ECMWF) Reanalysis-Land (ERA5-Land) as a reference. Corrected data were used to drive 11 widely validated empirical photovoltaic models to evaluate the spatiotemporal evolution of photovoltaic power potential in Xinjiang from 2000 to 2060. The results showed that average annual power potential from 2000 to 2020 was 282.29 kWh/m², with higher values in the south and lower in the north and a slight decreasing trend. In terms of future forecasting, compared with historical baseline period, photovoltaic power potential increased slightly under the Shared Socioeconomic Pathway (SSP)1-2.6 scenario, with about 79.41% of the land area showing favorable conditions of increased photovoltaic power potential and reduced coefficient of variation (CV). In contrast, under the SSP2-4.5 and SSP5-8.5 scenarios, unfavorable pattern of decreased photovoltaic power potential and increased CV expanded significantly, covering about 24.66% and 37.65% of the total area, respectively. Analysis of influencing factors showed that solar radiation was the dominant factor controlling photovoltaic power potential, while rising temperature had a negative effect. Under the SSP5-8.5 scenario, reduced solar radiation and rising temperature were the main factors driving the decline in photovoltaic power potential. At the regional level, although the central area currently had relatively low potential, it shows a decreasing CV and continuously increasing photovoltaic power potential in the future, indicating better conditions for development. Economic analysis showed that, under standard carbon price, the levelized cost of electricity (LCOE) on the SSP1-2.6 path was the lowest, yielding significant net social benefits. This study indicates that pursuing a sustainable development path is a key to mitigating the adverse impacts of climate change, providing scientific reference for medium- and long-term photovoltaic installation planning in Xinjiang.

Key words： photovoltaic power potential climate change variability empirical photovoltaic models the sixth phase of the Coupled Model Intercomparison Project (CMIP6) Shared Socioeconomic Pathway (SSP) scenarios levelized cost of energy

Received: 19 December 2025 Published: 31 July 2026

Corresponding Authors: ^*ZHANG Haiwei (E-mail: zhanghw@xju.edu.cn)

About author: First author contact:Author contributions

Conceptualization: MENG Nana, ZHANG Haiwei, LI Ran; Methodology: MENG Nana, LI Ran, ZHANG Haiwei, FAN Shichen; Formal analysis: MENG Nana, ZHANG Haiwei, PENG Yimo; Data curation: MENG Nana, GE Tianxu, ZHANG Haiwei, FAN Shichen; Writing - original draft preparation: MENG Nana, LI Ran; Writing - review and editing: MENG Nana, LI Ran, ZHANG Haiwei; Funding acquisition: MENG Nana, ZHANG Haiwei; Resources: MENG Nana, LI Ran, PENG Yimo, ZHANG Haiwei; Supervision: ZHANG Haiwei; Visualization: MENG Nana; Investigation: MENG Nana, ZHANG Haiwei, LI Ran; Validation: MENG Nana, ZHANG Haiwei, LI Ran; Software: MENG Nana; Project administration: ZHANG Haiwei. All authors approved the manuscript.

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Cite this article:

MENG Nana, LI Ran, PENG Yimo, GE Tianxu, FAN Shichen, ZHANG Haiwei. Photovoltaic power potential of variability and its influencing factors in Xinjiang, China. Journal of Arid Land, 2026, 18(7): 1258-1282.

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http://jal.xjegi.com/10.1016/j.jaridl.2026.07.005 OR http://jal.xjegi.com/Y2026/V18/I7/1258

Fig. 1 Overview of Xinjiang Uygur Autonomous Region (hereinafter referred to as Xinjiang) based on elevation data. DEM, digital elevation model. The figure is based on the standard map (GS(2024)0650) provided by the National Platform for Common Geospatial Information Services (http://bzdt.ch.mnr.gov.cn/). The boundary of the base map has not been modified.

Table S1 A list of the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models

Model		Formula	Parameter			Reference
Model		Formula	R-related	T-related	W-related	Reference
1	$P=R\times {{\text{ }\!\!\eta\!\!\text{ }}_{\text{ref}}}\left[ 1-\text{ }\!\!\beta\!\!\text{ }\left( {{T}_{\text{cell}}}-{{\text{T}}_{\text{ref}}} \right) \right]$		b_h and R_STC	β, T_cell, T_ref, and NOCT	W₁ and c_h	Mora Segado et al. (2015)
1	${{T}_{\text{cell}}}=T+{{\text{b}}_{\text{h}}}\left[ \frac{R}{{{\text{R}}_{\text{STC}}}}\left( \text{NOCT}-20 \right) \right]+{{\text{c}}_{\text{h}}}\left( W-{{\text{W}}_{1}} \right)$		b_h and R_STC	β, T_cell, T_ref, and NOCT	W₁ and c_h	Mora Segado et al. (2015)
2	$P=R\times {{\text{ }\!\!\eta\!\!\text{ }}_{\text{ref}}}\left[ 1-\text{ }\!\!\beta\!\!\text{ }\left( {{T}_{\text{cell}}}-{{\text{T}}_{\text{ref}}} \right) \right]$		c₃	β, T_cell, T_ref, c_1, and c₂	c₄	Pérez et al. (2019)
2	${{T}_{\text{cell}}}\text{= }{{\text{c}}_{\text{2}}}T\text{ + }{{\text{c}}_{\text{3}}}R\text{ + }{{\text{c}}_{\text{4}}}W\text{ + }{{\text{c}}_{\text{1}}}$		c₃	β, T_cell, T_ref, c_1, and c₂	c₄	Pérez et al. (2019)
3	$P=R\times {{\text{ }\!\!\eta\!\!\text{ }}_{\text{ref}}}\left[ 1-\text{ }\!\!\beta\!\!\text{ }\left( {{T}_{\text{cell}}}-{{\text{T}}_{\text{ref}}} \right) \right]$		c₃	β, T_cell, T_ref, c₁, and c₂	c₄	Feron et al. (2021)
3	${{T}_{\text{cell}}}\text{= }{{\text{c}}_{\text{1}}}\text{+ }{{\text{c}}_{\text{2}}}T\text{ + }{{\text{c}}_{\text{3}}}R-{{\text{c}}_{\text{4}}}W$		c₃	β, T_cell, T_ref, c₁, and c₂	c₄	Feron et al. (2021)
4	$P=R\times {{\text{ }\!\!\eta\!\!\text{ }}_{\text{ref}}}\left[ 1-\text{ }\!\!\beta\!\!\text{ }\left( {{T}_{\text{cell}}}-{{\text{T}}_{\text{ref}}} \right) \right]$		R_STC	β, T_cell, T_ref, and NOCT		Bayrakci et al. (2014)
4	${{T}_{cell}}=T+\left( \operatorname{NOCT}-20 \right)\frac{R}{{{\text{R}}_{STC}}}$		R_STC	β, T_cell, T_ref, and NOCT		Bayrakci et al. (2014)
5	$P=R\times {{\text{ }\!\!\eta\!\!\text{ }}_{\text{ref}}}\left[ 1-\text{ }\!\!\beta\!\!\text{ }\left( {{T}_{\text{cell}}}-{{\text{T}}_{\text{ref}}} \right)+\text{ }\!\!\gamma\!\!\text{ }\times \lg R \right]$		R_STC and γ	β, T_cell, T_ref, and NOCT		Zou et al. (2019)
5	${{T}_{cell}}=T+\left( \operatorname{NOCT}-20 \right)\frac{R}{{{\text{R}}_{STC}}}$		R_STC and γ	β, T_cell, T_ref, and NOCT		Zou et al. (2019)
6	$P=R\times {{\text{ }\!\!\eta\!\!\text{ }}_{\text{ref}}}\left[ 1-\text{ }\!\!\beta\!\!\text{ }\left( {{T}_{\text{cell}}}-{{\text{T}}_{\text{ref}}} \right)+\text{ }\!\!\gamma\!\!\text{ }\times \lg {{R}_{T}} \right]$		R_STC, γ, and R_T	β, T_cell, T_ref, and NOCT		Kan et al. (2021)
6	${{T}_{cell}}=T+\left( \operatorname{NOCT}-20 \right)\frac{{{R}_{T}}}{{{\text{R}}_{STC}}}$		R_STC, γ, and R_T	β, T_cell, T_ref, and NOCT		Kan et al. (2021)
7	$P=R\times {{\text{ }\!\!\eta\!\!\text{ }}_{\text{ref}}}\left[ 1-\text{ }\!\!\beta\!\!\text{ }\left( {{T}_{\text{cell}}}-{{\text{T}}_{\text{ref}}} \right)+\text{ }\!\!\gamma\!\!\text{ }\times \lg R \right]$		γ and c₃	β, T_cell, T_ref, c₁, and c₂		Feng et al. (2021)
7	${{T}_{\text{cell}}}\text{=}{{\operatorname{c}}_{\text{1}}}\text{+ }{{\text{c}}_{\text{2}}}T\text{ + }{{\text{c}}_{\text{3}}}R\text{ }$		γ and c₃	β, T_cell, T_ref, c₁, and c₂		Feng et al. (2021)
8	$P=R\times {{\text{ }\!\!\eta\!\!\text{ }}_{\text{ref}}}\left[ 1-\text{ }\!\!\beta\!\!\text{ }\left( {{T}_{\text{cell}}}-{{\text{T}}_{\text{ref}}} \right) \right]$		R_STC	β, T_cell, T_ref, and NOCT		Davis et al. (2001)
8	${{T}_{cell}}=T+\frac{R}{{{\text{R}}_{STC}}}\left( \operatorname{NOCT}-{{\text{T}}_{\text{ref}}} \right)\left( 1-\frac{{{\text{ }\!\!\eta\!\!\text{ }}_{\text{ref}}}}{\text{ }\!\!\tau\!\!\text{ a}} \right)$		R_STC	β, T_cell, T_ref, and NOCT		Davis et al. (2001)
9	$\begin{matrix} P\text{ = }R\times {{\text{ }\!\!\eta\!\!\text{ }}_{\text{ref}}}[\left( \text{1 + }{{\text{ }\!\!\gamma\!\!\text{ }}_{\text{1}}}T \right)({{\text{A}}_{\text{1}}}^{-\text{exp}\left[ {{R}^{\text{1}{{\text{0}}^{\text{3}}}}} \right]}+ \\ {{\text{A}}_{\text{2}}}R\text{ + }{{\text{A}}_{\text{3}}}\times \ln R)] \\ \end{matrix}$		A₁, A₂, and A₃	γ₁		Soler-Castillo et al. (2021)
9	${{T}_{cell}}=T+\left( \operatorname{NOCT}-20 \right)\frac{R}{{{\text{R}}_{STC}}}$		R_STC	γ₁		Soler-Castillo et al. (2021)
10	$\begin{matrix} P\text{ = }R\times {{\eta }_{ref}}\text{ }\!\![\!\!\text{ 1 + }{{\text{k}}_{\text{1}}}\times \ln R'\text{+ }{{\text{k}}_{\text{2}}}\times {{\left( \text{l}nR' \right)}^{\text{2}}}\text{ + } \\ T'\text{ }\!\![\!\!\text{ }{{\text{k}}_{\text{3}}}\text{+}{{\text{k}}_{\text{4}}}\times \ln R'+{{\text{k}}_{\text{5}}}\times {{\left( \text{l}nR' \right)}^{\text{2}}}\text{+}{{\text{k}}_{\text{6}}}\times T{{'}^{\text{2}}}]] \\ \end{matrix}$		k₁, k₂, R', R_STC, and c_T	k₃, k₄, k₅, k₆, T', T_mod, and T_ref		Sweerts et al. (2019)
	$R'=\frac{R}{{{\text{R}}_{STC}}}$
	$T'\text{= }{{T}_{\bmod }}-\text{ }{{\text{T}}_{\text{ref}}}$
	${{T}_{\bmod }}\text{= }T\text{ + }{{\text{c}}_{\text{T}}}R$
11	$P\text{ = }R\times {{\text{ }\!\!\eta\!\!\text{ }}_{\text{ref}}}$					Cheng et al. (2020)

Table 1 Summary of empirical models for photovoltaic power potential

Table S2 Values of constant parameters in the 11 photovoltaic models shown in Table 1

Fig. 2 Taylor diagrams for the surface downward shortwave radiation (RSDS), near-surface wind speed at a height of 10 m (sfcWind), and near-surface air temperature at a height of 2 m (TAS) in Xinjiang during the historical baseline period (2000-2020), compared against reference data, before (a) and after (b) correction. The red star symbol represents the reference fields of the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis-Land (ERA5-Land). The azimuth angle corresponds to the Pearson correlation coefficient of the spatial pattern. The radial distance from the origin indicates the ratio of the standard deviation between simulations and the reference. The distance from a simulation point to the reference point represents the centered root mean square error (RMSE).

Fig. 3 Spatial distribution of multi-year annual means of RSDS (a), TAS (b), and sfcWind (c) derived from ERA5-Land for the historical baseline period (2000-2020)

Fig. 4 Absolute changes of RSDS (a-c), TAS (d-f), and sfcWind (g-i) in Xinjiang during 2021-2060 under different Shared Socioeconomic Pathways (SSPs)

Fig. 5 Inter-annual variation of sfcWind (a), TAS (b), and RSDS (c)

Fig. 6 Spatial distribution of the annual average photovoltaic power potential in Xinjiang during 2000-2020 evaluated by 11 different photovoltaic models (a-k) and mean of multi-model (l)

Fig. 7 Spatial patterns of the annual average photovoltaic power potential during 2000-2020 (a) and Sen-Mann-Kendall (MK) trend test results (b), and temporal inter-annual variation (c)

Fig. 8 Trends in the photovoltaic power potential in Xinjiang during 2000-2060 under three SSPs

Fig. 9 Analysis of the variability of the photovoltaic power potential in Xinjiang during the future carbon-neutral window (2021-2060) under three SSPs. (a-c), absolute change in the annual photovoltaic power potential relative to the historical baseline period (2000-2020); (d-f), relative change normalized by the historical mean from the average difference between 2021-2060 and 2000-2020; (g-i), trend of change in the coefficient of variation (CV); (j-l), Sen-MK spatial trend distribution of photovoltaic power potential during 2021-2060.

Fig. 10 Four combined patterns of photovoltaic and CV trends during 2021-2060 under three SSPs. (a-c), spatial distribution of the four trend combinations; (d), area proportion statistics of the four classified patterns presented by stacked bar charts. Dashed areas represent regions with statistically significant at P<0.05 level.

Fig. 11 Spatial patterns of the linear regression coefficients between climatic factors and photovoltaic power potential in Xinjiang. (a-d), RSDS; (e-h), sfcWind; (i-l), TAS. Areas covered with dots indicate statistical significance at P<0.05 level.

Fig. 12 Levelized cost of energy (LCOE) of photovoltaic power potential in Xinjiang under different scenarios. (a), LCOE comparison with and without carbon benefits under a carbon social cost of 70 USD/t CO₂; (b), sensitivity of LCOE to carbon social cost across SSP scenarios.


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