PMID- 38413519
OWN - NLM
STAT- MEDLINE
DCOM- 20240408
LR  - 20240417
IS  - 1614-7499 (Electronic)
IS  - 0944-1344 (Linking)
VI  - 31
IP  - 15
DP  - 2024 Mar
TI  - Geographic information system-based multi-criteria decision-making analysis for 
      investment assessment of wind-photovoltaic-shared energy storage power stations: 
      a case study of Shanxi Province.
PG  - 22604-22629
LID - 10.1007/s11356-024-32123-5 [doi]
AB  - As the center of the development of power industry, wind-photovoltaic (PV)-shared 
      energy storage project is the key tool for achieving energy transformation. This 
      research seeks to construct a feasible model for investment appraisal of 
      wind-PV-shared energy storage power stations by combining geographic information 
      system (GIS) and multi-criteria decision-making (MCDM) method. Firstly, a 
      comprehensive criteria system is established from the perspectives of orography, 
      economy, resources, climate, and society, and the evaluation data is described 
      using probabilistic linguistic term sets (PLTSs). Then, to avoid the weight 
      deviation produced by the single weighting approach, a comprehensive weighting 
      model including the best-worst method (BWM) and entropy weight method is provided 
      to calculate the weights of criteria. Next, expert weights are calculated based 
      on trust analysis. Finally, alternatives are ranked by the improved gained and 
      lost dominance score (GLDS) method. To verify the validity of the model, an 
      empirical investigation is carried out in Shanxi Province. The results show that 
      the economy is the primary factor influencing the investment decision. Among all 
      the projects approved by the government, alternative F(4) located in Yanzhuang 
      Town, Yuanping City is the best investment object. Furthermore, to illustrate the 
      stability of the result, triple sensitivity analysis and comparative analysis are 
      conducted in Shanxi Province. This study expands the application scope of GIS and 
      MCDM method by first providing support for government and investors to identify 
      optimal investment targets.
CI  - (c) 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, 
      part of Springer Nature.
FAU - Wang, Yaping
AU  - Wang Y
AD  - School of Economics and Management, North China Electric Power University, 
      Beijing, 102206, China.
FAU - Gao, Jianwei
AU  - Gao J
AD  - School of Economics and Management, North China Electric Power University, 
      Beijing, 102206, China. gaojianwei111@sina.com.
FAU - Wei, Lingli
AU  - Wei L
AD  - School of Economics and Management, North China Electric Power University, 
      Beijing, 102206, China.
FAU - Wu, Haoyu
AU  - Wu H
AD  - School of Economics and Management, North China Electric Power University, 
      Beijing, 102206, China.
FAU - Zhao, Shutong
AU  - Zhao S
AD  - School of Economics and Management, North China Electric Power University, 
      Beijing, 102206, China.
LA  - eng
PT  - Journal Article
DEP - 20240227
PL  - Germany
TA  - Environ Sci Pollut Res Int
JT  - Environmental science and pollution research international
JID - 9441769
SB  - IM
MH  - Cities
MH  - Climate
MH  - *Geographic Information Systems
MH  - Investments
MH  - *Wind
MH  - Humans
OTO - NOTNLM
OT  - GIS
OT  - Investment assessment
OT  - MCDM
OT  - The improved GLDS method
OT  - Wind-PV-shared energy storage power stations
EDAT- 2024/02/28 00:43
MHDA- 2024/04/08 06:43
CRDT- 2024/02/27 23:19
PHST- 2023/05/22 00:00 [received]
PHST- 2024/01/16 00:00 [accepted]
PHST- 2024/04/08 06:43 [medline]
PHST- 2024/02/28 00:43 [pubmed]
PHST- 2024/02/27 23:19 [entrez]
AID - 10.1007/s11356-024-32123-5 [pii]
AID - 10.1007/s11356-024-32123-5 [doi]
PST - ppublish
SO  - Environ Sci Pollut Res Int. 2024 Mar;31(15):22604-22629. doi: 
      10.1007/s11356-024-32123-5. Epub 2024 Feb 27.