Data, Distribution, and Modeling Innovations in Spatiotemporal Energy Economics

Halkos, George (2025): Data, Distribution, and Modeling Innovations in Spatiotemporal Energy Economics.

Abstract

The objective of the present review is to synthesize recent state-of-the-art advances in the field of energy economics. The present review aims to elucidate the interconnections among various applicable and practical methodologies that may facilitate a sustainable energy transition and therefore the novelty lies in the cross-cutting, methodological integration and forward-looking perspective that informs both academic research and practical policy development in the context of sustainable energy transitions. The contribution of this review is fourfold. First, it systematically compiles the core empirical advancements within different sectors of the energy domain, providing a structured assessment of contemporary research efforts. Second, it critically examines the challenges associated with data availability and reviews methodological innovations designed to address these limitations. Third, it consolidates developments in spatiotemporal econometric techniques, highlighting their significance in capturing dynamic spatial and temporal dimensions of energy systems. Fourth, it presents emerging machine learning-based approaches for forecasting, underscoring their potential to enhance predictive capabilities and inform policy and investment decisions. By integrating insights across these domains, the review offers a comprehensive framework for understanding the methodological evolution in energy economics and identifies pathways for future research that support the global pursuit of a sustainable energy future.

• Item Type: MPRA Paper
• Language: English
• Keywords: energy economics; energy policy; energy transition; energy modelling and forecasting.
• Subjects: Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q0 - General; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q0 - General > Q01 - Sustainable Development; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q40 - General; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q43 - Energy and the Macroeconomy; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q47 - Energy Forecasting; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q48 - Government Policy; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q5 - Environmental Economics > Q51 - Valuation of Environmental Effects

References

1. Halkos, G.E.; Aslanidis, P.-S.C. Green Energy Pathways Towards Carbon Neutrality. Environ. Resour. Econ. 2024, doi:10.1007/s10640-024-00856-z.

2. Nurdiawati, A.; Urban, F. Towards Deep Decarbonisation of Energy-Intensive Industries: A Review of Current Status, Technologies and Policies. Energies 2021, 14, 2408, doi:10.3390/en14092408.

3. Halkos, G.E.; Aslanidis, P.-S.C. Sustainable Energy Development in an Era of Geopolitical Multi-Crisis. Applying Productivity Indices within Institutional Framework. Resour. Policy 2023, 85, 103854, doi:10.1016/j.resourpol.2023.103854.

4. Halkos, G.E.; Tzeremes, N.G. Measuring the Effect of Kyoto Protocol Agreement on Countries’ Environmental Efficiency in CO2 Emissions: An Application of Conditional Full Frontiers. J. Product. Anal. 2014, 41, 367–382, doi:10.1007/s11123-013-0343-1.

5. Halkos, G.E.; Kitsou, D.C. Weighted Location Differential Tax in Environmental Problems. Environ. Econ. Policy Stud. 2018, 20, 1–15, doi:10.1007/s10018-016-0179-9.

6. Halkos, G.E.; Moll de Alba, J.; Aslanidis, P.-S.C. Examining Energy and Carbon Intensity of the European Fashion Industry: A Hybrid DEA Analysis for Circular Economy. Circ. Econ. Sustain. 2024, doi:10.1007/s43615-024-00488-x.

7. Halkos, G.E.; Tsirivis, A.S. Electricity Prices in the European Union Region: The Role of Renewable Energy Sources, Key Economic Factors and Market Liberalization. Energies 2023, 16, 2540, doi:10.3390/en16062540.

8. Halkos, G.E.; Aslanidis, P.-S.C. Addressing Multidimensional Energy Poverty Implications on Achieving Sustainable Development. Energies 2023, 16, 3805, doi:10.3390/en16093805.

9. Halkos, G.E.; Tsirivis, A.S. Sustainable Development of the European Electricity Sector: Investigating the Impact of Electricity Price, Market Liberalization and Energy Taxation on RES Deployment. Energies 2023, 16, 5567, doi:10.3390/en16145567.

10. Ma, T.; Duan, F.; He, K.; Qin, Y.; Tong, D.; Geng, G.; Liu, X.; Li, H.; Yang, S.; Ye, S.; et al. Air Pollution Characteristics and Their Relationship with Emissions and Meteorology in the Yangtze River Delta Region during 2014–2016. J. Environ. Sci. 2019, 83, 8–20, doi:10.1016/j.jes.2019.02.031.

11. Espinosa, R.; Jiménez, F.; Palma, J. Multi-Objective Evolutionary Spatio-Temporal Forecasting of Air Pollution. Futur. Gener. Comput. Syst. 2022, 136, 15–33, doi:10.1016/j.future.2022.05.020.

12. Deb, K. Multi-Objective Optimization Using Evolutionary Algorithms; JohnWiley & Sons, Inc., 2001.

13. Ehrgott, M. Multicriteria Optimization, in: Lecture Notes in Economics Andmathematical Systems; 2nd ed.; Springer: Berlin, Heidelberg, 2005.

14. Deb, K.; Pratap, A.; Agarwal, S.; Meyarivan, T. A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II. IEEE Trans. Evol. Comput. 2002, 6, 182–197, doi:10.1109/4235.996017.

15. Qingfu Zhang; Hui Li MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Trans. Evol. Comput. 2007, 11, 712–731, doi:10.1109/TEVC.2007.892759.

16. Zitzler, E.; Laumanns, M.; Thiele, L. SPEA2: Improving the Strength Pareto Evolutionary Algorithm. TIK Rep. 2001, doi:https://doi.org/10.3929/ethz-a-004284029.

17. Dai, H.; Huang, G.; Zeng, H. Multi-Objective Optimal Dispatch Strategy for Power Systems with Spatio-Temporal Distribution of Air Pollutants. Sustain. Cities Soc. 2023, 98, 104801, doi:10.1016/j.scs.2023.104801.

18. Gupta, R.; Nanda, S.J. Solving Time Varying Many-Objective TSP with Dynamic <math Altimg="si56.Svg" Display="inline" Id="d1e4589"> <mi>θ</Mi> </Math> -NSGA-III Algorithm. Appl. Soft Comput. 2022, 118, 108493, doi:10.1016/j.asoc.2022.108493.

19. Yang, S.; Li, M.; Liu, X.; Zheng, J. A Grid-Based Evolutionary Algorithm for Many-Objective Optimization. IEEE Trans. Evol. Comput. 2013, 17, 721–736, doi:10.1109/TEVC.2012.2227145.

20. Liu, Z.-Z.; Wang, Y.; Wang, B.-C. Indicator-Based Constrained Multiobjective Evolutionary Algorithms. IEEE Trans. Syst. Man, Cybern. Syst. 2021, 51, 5414–5426, doi:10.1109/TSMC.2019.2954491.

21. Gao, W.; Xu, W.; Gong, M.; Yen, G.G. A Decomposition-Based Evolutionary Algorithm Using an Estimation Strategy for Multimodal Multi-Objective Optimization. Inf. Sci. (Ny). 2022, 606, 531–548, doi:10.1016/j.ins.2022.05.075.

22. Wang, Y.; Liu, P.; Yao, Y. BMW-TOPSIS: A Generalized TOPSIS Model Based on Three-Way Decision. Inf. Sci. (Ny). 2022, 607, 799–818, doi:10.1016/j.ins.2022.06.018.

23. Wu, R.; Li, Z.; Wang, S. The Varying Driving Forces of Urban Land Expansion in China: Insights from a Spatial-Temporal Analysis. Sci. Total Environ. 2021, 766, 142591, doi:10.1016/j.scitotenv.2020.142591.

24. Xu, Y.; Zhang, W.; Wang, J.; Ji, S.; Wang, C.; Streets, D.G. Investigating the Spatially Heterogeneous Impacts of Urbanization on City-Level Industrial SO2 Emissions: Evidence from Night-Time Light Data in China. Ecol. Indic. 2021, 133, 108430, doi:10.1016/j.ecolind.2021.108430.

25. Xu, Y.; Liu, Z.; Walker, T.R.; Adams, M.; Dong, H. Spatio-Temporal Patterns and Spillover Effects of Synergy on Carbon Dioxide Emission and Pollution Reductions in the Yangtze River Delta Region in China. Sustain. Cities Soc. 2024, 107, 105419, doi:10.1016/j.scs.2024.105419.

26. Ang, B.W. The LMDI Approach to Decomposition Analysis: A Practical Guide. Energy Policy 2005, 33, 867–871, doi:10.1016/j.enpol.2003.10.010.

27. Shan, Y.; Guan, Y.; Hang, Y.; Zheng, H.; Li, Y.; Guan, D.; Li, J.; Zhou, Y.; Li, L.; Hubacek, K. City-Level Emission Peak and Drivers in China. Sci. Bull. 2022, 67, 1910–1920, doi:10.1016/j.scib.2022.08.024.

28. Long, H.; Lin, B.; Ou, Y.; Chen, Q. Spatio-Temporal Analysis of Driving Factors of Water Resources Consumption in China. Sci. Total Environ. 2019, 690, 1321–1330, doi:10.1016/j.scitotenv.2019.06.311.

29. Zhang, J.; Zhang, K.; Zhao, F. Spatial Effects of Economic Growth, Energy Consumption and Environmental Pollution in the Provinces of China—An Empirical Study of a Spatial Econometrics Model. Sustain. Dev. 2020, 28, 868–879, doi:10.1002/sd.2042.

30. Zhang, P.; Yang, D.; Qin, M.; Jing, W. Spatial Heterogeneity Analysis and Driving Forces Exploring of Built-up Land Development Intensity in Chinese Prefecture-Level Cities and Implications for Future Urban Land Intensive Use. Land use policy 2020, 99, 104958, doi:10.1016/j.landusepol.2020.104958.

31. Duman, Z.; Mao, X.; Cai, B.; Zhang, Q.; Chen, Y.; Gao, Y.; Guo, Z. Exploring the Spatiotemporal Pattern Evolution of Carbon Emissions and Air Pollution in Chinese Cities. J. Environ. Manage. 2023, 345, 118870, doi:10.1016/j.jenvman.2023.118870.

32. Wang, Z.; Li, J.; Liang, L. Spatio-Temporal Evolution of Ozone Pollution and Its Influencing Factors in the Beijing-Tianjin-Hebei Urban Agglomeration. Environ. Pollut. 2020, 256, 113419, doi:10.1016/j.envpol.2019.113419.

33. Dong, F.; Wang, Y.; Zheng, L.; Li, J.; Xie, S. Can Industrial Agglomeration Promote Pollution Agglomeration? Evidence from China. J. Clean. Prod. 2020, 246, 118960, doi:10.1016/j.jclepro.2019.118960.

34. Xu, H.L. and Deng, Y.P. Does Foreign Direct Investment Lead to Environmental Pollution in China? A Spatial Econometric Study Based on Chinese Provincial Panel Data. Manage. World 2012, 2, 31–43, doi:https://doi.org/10.19744/j.cnki.11-1235/f.2012.02.004.

35. Wang, S.; Fang, C.; Wang, Y. Spatiotemporal Variations of Energy-Related CO 2 Emissions in China and Its Influencing Factors: An Empirical Analysis Based on Provincial Panel Data. Renew. Sustain. Energy Rev. 2016, 55, 505–515, doi:10.1016/j.rser.2015.10.140.

36. Mi, K.; Zhuang, R.; Zhang, Z.; Gao, J.; Pei, Q. Spatiotemporal Characteristics of PM2.5 and Its Associated Gas Pollutants, a Case in China. Sustain. Cities Soc. 2019, 45, 287–295, doi:10.1016/j.scs.2018.11.004.

37. Su, Y.; Yu, Y. Spatial Agglomeration of New Energy Industries on the Performance of Regional Pollution Control through Spatial Econometric Analysis. Sci. Total Environ. 2020, 704, 135261, doi:10.1016/j.scitotenv.2019.135261.

38. Yan, D.; Kong, Y.; Jiang, P.; Huang, R.; Ye, B. How Do Socioeconomic Factors Influence Urban PM2.5 Pollution in China? Empirical Analysis from the Perspective of Spatiotemporal Disequilibrium. Sci. Total Environ. 2021, 761, 143266, doi:10.1016/j.scitotenv.2020.143266.

39. Tang, J.; Tong, M.; Sun, Y.; Du, J.; Liu, N. A Spatio-Temporal Perspective of China’s Industrial Circular Economy Development. Sci. Total Environ. 2020, 706, 135754, doi:10.1016/j.scitotenv.2019.135754.

40. Moran, P.A.P. The Interpretation of Statistical Maps. J. R. Stat. Soc. Ser. B Stat. Methodol. 1948, 10, 243–251, doi:10.1111/j.2517-6161.1948.tb00012.x.

41. Geary, R.C. The Contiguity Ratio and Statistical Mapping. Inc. Stat. 1954, 5, 115, doi:10.2307/2986645.

42. Tepanosyan, G.; Sahakyan, L.; Zhang, C.; Saghatelyan, A. The Application of Local Moran’s I to Identify Spatial Clusters and Hot Spots of Pb, Mo and Ti in Urban Soils of Yerevan. Appl. Geochemistry 2019, 104, 116–123, doi:10.1016/j.apgeochem.2019.03.022.

43. Ren, H.; Shang, Y.; Zhang, S. Measuring the Spatiotemporal Variations of Vegetation Net Primary Productivity in Inner Mongolia Using Spatial Autocorrelation. Ecol. Indic. 2020, 112, 106108, doi:10.1016/j.ecolind.2020.106108.

44. Ma, W.; Wei, F.; Zhang, J.; Karthe, D.; Opp, C. Green Water Appropriation of the Cropland Ecosystem in China. Sci. Total Environ. 2022, 806, 150597, doi:10.1016/j.scitotenv.2021.150597.

45. Wang, N.; Fu, X.; Wang, S. Spatial-Temporal Variation and Coupling Analysis of Residential Energy Consumption and Economic Growth in China. Appl. Energy 2022, 309, 118504, doi:10.1016/j.apenergy.2021.118504.

46. Li, J.; Heap, A.D. Spatial Interpolation Methods Applied in the Environmental Sciences: A Review. Environ. Model. Softw. 2014, 53, 173–189, doi:10.1016/j.envsoft.2013.12.008.

47. Zhang, H.; Zhan, Y.; Li, J.; Chao, C.-Y.; Liu, Q.; Wang, C.; Jia, S.; Ma, L.; Biswas, P. Using Kriging Incorporated with Wind Direction to Investigate Ground-Level PM2.5 Concentration. Sci. Total Environ. 2021, 751, 141813, doi:10.1016/j.scitotenv.2020.141813.

48. Liang, L.; Wang, Z. Control Models and Spatiotemporal Characteristics of Air Pollution in the Rapidly Developing Urban Agglomerations. Int. J. Environ. Res. Public Health 2021, 18, 6177, doi:10.3390/ijerph18116177.

49. Li, J.; Heap, A.D. A Review of Comparative Studies of Spatial Interpolation Methods in Environmental Sciences: Performance and Impact Factors. Ecol. Inform. 2011, 6, 228–241, doi:10.1016/j.ecoinf.2010.12.003.

50. Li, J.; Heap, A.D. A Review of Spatial Interpolation Methods for Environmental Scientists. 2008.

51. Van Brummelen, G. Heavenly Mathematics: The Forgotten Art of Spherical Trigonometry; Princeton University Press., 2017;

52. Hengl, T. A Practical Guide to Geostatistical Mapping of Environmental Variables. JRC Scientific and Technichal Reports. Off. Off. Publ. Eur. Communities, Luxemb. 2007.

53. Minasny, B.; McBratney, A.B. Spatial Prediction of Soil Properties Using EBLUP with the Matérn Covariance Function. Geoderma 2007, 140, 324–336, doi:10.1016/j.geoderma.2007.04.028.

54. Jiang, W.; Gao, W.; Gao, X.; Ma, M.; Zhou, M.; Du, K.; Ma, X. Spatio-Temporal Heterogeneity of Air Pollution and Its Key Influencing Factors in the Yellow River Economic Belt of China from 2014 to 2019. J. Environ. Manage. 2021, 296, 113172, doi:10.1016/j.jenvman.2021.113172.

55. Wei, Q.; Zhang, L.; Duan, W.; Zhen, Z. Global and Geographically and Temporally Weighted Regression Models for Modeling PM2.5 in Heilongjiang, China from 2015 to 2018. Int. J. Environ. Res. Public Health 2019, 16, 5107, doi:10.3390/ijerph16245107.

56. Wei, P.; Xie, S.; Huang, L.; Liu, L.; Tang, Y.; Zhang, Y.; Wu, H.; Xue, Z.; Ren, D. Spatial Interpolation of PM2.5 Concentrations during Holidays in South-Central China Considering Multiple Factors. Atmos. Pollut. Res. 2022, 13, 101480, doi:10.1016/j.apr.2022.101480.

57. Zhu, Y.; Wang, Z.; Zhu, L. Spatiotemporal Effects of Renewable Energy Technology Innovation on Industrial Cleaner Production: A Geographically Temporally Weighted Analysis. J. Clean. Prod. 2021, 312, 127783, doi:10.1016/j.jclepro.2021.127783.

58. Huang, B.; Wu, B.; Barry, M. Geographically and Temporally Weighted Regression for Modeling Spatio-Temporal Variation in House Prices. Int. J. Geogr. Inf. Sci. 2010, 24, 383–401, doi:10.1080/13658810802672469.

59. Yu, D. Understanding Regional Development Mechanisms in Greater Beijing Area, China, 1995–2001, from a Spatial–Temporal Perspective. GeoJournal 2014, 79, 195–207, doi:10.1007/s10708-013-9500-3.

60. Rodrigues, M.; Jiménez-Ruano, A.; Peña-Angulo, D.; de la Riva, J. A Comprehensive Spatial-Temporal Analysis of Driving Factors of Human-Caused Wildfires in Spain Using Geographically Weighted Logistic Regression. J. Environ. Manage. 2018, 225, 177–192, doi:10.1016/j.jenvman.2018.07.098.

61. Cheng, J.; Dai, S.; Ye, X. Spatiotemporal Heterogeneity of Industrial Pollution in China. China Econ. Rev. 2016, 40, 179–191, doi:10.1016/j.chieco.2016.07.001.

62. Shi, T.; Zhang, W.; Zhou, Q.; Wang, K. Industrial Structure, Urban Governance and Haze Pollution: Spatiotemporal Evidence from China. Sci. Total Environ. 2020, 742, 139228, doi:10.1016/j.scitotenv.2020.139228.

63. Zhou, X.; Zhang, X.; Wang, Y.; Chen, W.; Li, Q. Spatio-Temporal Variations and Socio-Economic Drivers of Air Pollution: Evidence from 332 Chinese Prefecture-Level Cities. Atmos. Pollut. Res. 2023, 14, 101782, doi:10.1016/j.apr.2023.101782.

64. Jiang, F.; Chen, B.; Li, P.; Jiang, J.; Zhang, Q.; Wang, J.; Deng, J. Spatio-Temporal Evolution and Influencing Factors of Synergizing the Reduction of Pollution and Carbon Emissions - Utilizing Multi-Source Remote Sensing Data and GTWR Model. Environ. Res. 2023, 229, 115775, doi:10.1016/j.envres.2023.115775.

65. Fotheringham, A.S.; Crespo, R.; Yao, J. Geographical and Temporal Weighted Regression (GTWR). Geogr. Anal. 2015, 47, 431–452, doi:10.1111/gean.12071.

66. Wang, J.; Li, X.; Christakos, G.; Liao, Y.; Zhang, T.; Gu, X.; Zheng, X. Geographical Detectors‐Based Health Risk Assessment and Its Application in the Neural Tube Defects Study of the Heshun Region, China. Int. J. Geogr. Inf. Sci. 2010, 24, 107–127, doi:10.1080/13658810802443457.

67. Liu, M.; Hao, W. Spatial Distribution and Its Influencing Factors of National A-Level Tourist Attractions in Shanxi Province. Acta Geogr. Sin 2020, 75, 878–888, doi:https://doi.org/10.11821/dlxb202004015.

68. Artelaris, P. Local versus Regime Convergence Regression Models: A Comparison of Two Approaches. GeoJournal 2015, 80, 263–277, doi:10.1007/s10708-014-9551-0.

69. Huang, C.; Liu, K.; Zhou, L. Spatio-Temporal Trends and Influencing Factors of PM2.5 Concentrations in Urban Agglomerations in China between 2000 and 2016. Environ. Sci. Pollut. Res. 2021, 28, 10988–11000, doi:10.1007/s11356-020-11357-z.

70. Mu, L.; Fang, L.; Dou, W.; Wang, C.; Qu, X.; Yu, Y. Urbanization-Induced Spatio-Temporal Variation of Water Resources Utilization in Northwestern China: A Spatial Panel Model Based Approach. Ecol. Indic. 2021, 125, 107457, doi:10.1016/j.ecolind.2021.107457.

71. Fan, Q.; Yang, S.; Liu, S. Asymmetrically Spatial Effects of Urban Scale and Agglomeration on Haze Pollution in China. Int. J. Environ. Res. Public Health 2019, 16, 4936, doi:10.3390/ijerph16244936.

72. Breiman, L. Statistical Modeling: The Two Cultures (with Comments and a Rejoinder by the Author). Stat. Sci. 2001, 16, doi:10.1214/ss/1009213726.

73. Iskhakov, F.; Rust, J.; Schjerning, B. Machine Learning and Structural Econometrics: Contrasts and Synergies. Econom. J. 2020, 23, S81–S124, doi:10.1093/ectj/utaa019.

74. Athey, S.; Imbens, G.W. Machine Learning Methods That Economists Should Know About. Annu. Rev. Econom. 2019, 11, 685–725, doi:10.1146/annurev-economics-080217-053433.

75. Rust, J. Has Dynamic Programming Improved Decision Making? Annu. Rev. Econom. 2019, 11, 833–858, doi:10.1146/annurev-economics-080218-025721.

76. Seng, D.; Zhang, Q.; Zhang, X.; Chen, G.; Chen, X. Spatiotemporal Prediction of Air Quality Based on LSTM Neural Network. Alexandria Eng. J. 2021, 60, 2021–2032, doi:10.1016/j.aej.2020.12.009.

77. Zhang, K.; Zhang, X.; Song, H.; Pan, H.; Wang, B. Air Quality Prediction Model Based on Spatiotemporal Data Analysis and Metalearning. Wirel. Commun. Mob. Comput. 2021, 2021, doi:10.1155/2021/9627776.

78. Zhao, G.; Huang, G.; He, H.; He, H.; Ren, J. Regional Spatiotemporal Collaborative Prediction Model for Air Quality. IEEE Access 2019, 7, 134903–134919, doi:10.1109/ACCESS.2019.2941732.

79. Chae, S.; Shin, J.; Kwon, S.; Lee, S.; Kang, S.; Lee, D. PM10 and PM2.5 Real-Time Prediction Models Using an Interpolated Convolutional Neural Network. Sci. Rep. 2021, 11, 11952, doi:10.1038/s41598-021-91253-9.

80. Samal, K.; Babu, K.; Das, S. Spatio-Temporal Prediction of Air Quality Using Distance Based Interpolation and Deep Learning Techniques. EAI Endorsed Trans. Smart Cities 2018, 168139, doi:10.4108/eai.15-1-2021.168139.