All Issue

2021 Vol.30, Issue 3 Preview Page

Research Paper

September 2021. pp. 471-501
Abstract
We measure Environmental Efficiency (EE) based on CO2 in four income groups from 1998 to 2018, using the Meta Stochastic Frontier Analysis method by Input Distance Function. Our results showed that economic growth and energy consumption would increase carbon dioxide emissions, and increasing labor and capital input will reduce it. Moreover, we compared Group Environmental Efficiency (GEE), Meta Environmental Efficiency (MEE), and Environmental Gap Ratio (EGR). The results showed that GEEs were be overestimated. Furthermore, the MEE showed a downward trend during this period. The lower-middle-income group had the highest EGR performance. High-income and upper-middle-income groups showed less efficiency in MEE and EGR. To improve environmental efficiency, we must reduce fossil fuels and find more scientific and technological ways to solve existing environmental problems as soon as possible.
본 연구는 투입물 거리함수에 의한 메타확률 프런티어 방법을 사용하여 1998년부터 2018년까지 4개 소득그룹의 이산화탄소 기준의 환경효율성(EE)을 측정하였다. 에너지 소비가 이산화탄소 배출을 증가시키고, 노동과 자본투입을 증가하면 이산화탄소 배출을 감소시킬 것임을 보여주고 있다. 또한 그룹환경효율성(GEE: Group Environmental Efficiency), 메타환경효율성(MEE: Meta Environmental Efficiency) 및 환경격차(EGR: Environmental Gap Ratio)를 비교하였다. 결과는 GEE가 과대평가되고 MEE는 이 기간 동안 하향세를 보여주고 있다. 중하위 소득그룹의 EGR은 가장 높았다. 그리고 고소득 및 중상위 소득그룹의 MEE 및 EGR이 상대적으로 낮았다. 환경효율성을 높이려면 화석에너지를 저감하고 기준 환경 문제를 해결하는 방법이 보다 과학적이고 기술적인 방법을 찾아 필요가 있음을 시사한다.
References
  1. Aigner, D., C. K. Lovell, and P. Schmidt, “Formulation and estimation of stochastic frontier production function models,” Journal of Econometrics, Vol. 6, No. 1, 1977, pp. 21~37. 10.1016/0304-4076(77)90052-5
  2. Alcott, B., “Jevons' paradox,” Ecological Economics, Vol. 54, No. 1, 2005, pp. 9~21. 10.1016/j.ecolecon.2005.03.020
  3. Arcelus, F., and P. Arocena, “Productivity differences across OECD countries in the presence of environmental constraints,” Journal of the Operational Research Society, Vol. 56, No. 12, 2005, pp. 1352~1362. 10.1057/palgrave.jors.2601942
  4. Battese, G. E., and T. J. Coelli, “Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India,” Journal of Productivity Analysis, Vol. 3, No. 1, 1992, pp. 153~169. 10.1007/BF00158774
  5. Battese, G. E., and D. P. Rao, “Technology gap, efficiency, and a stochastic metafrontier function,” International Journal of Business and Economics, Vol. 1, No. 2, 2002, p. 87.
  6. Battese, G. E., D. P. Rao, and C. J. O'donnell, “A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies,” Journal of Productivity Analysis, Vol. 21, No. 1, 2004, pp. 91~103. 10.1023/B:PROD.0000012454.06094.29
  7. Battese, G. E., D. P. Rao, and D. Walujadi, Technical Efficiency and Productivity Potential of Garment Firms in Different Regions in Indonesia: A Stochastic Frontier Analysis Using a Time-varying Inefficiency Model and a Metaproduction Frontier, CEPA Working-Papers. No 7. School of Economics. University of New England. Australia, 2001.
  8. Bi, G. -B., W. Song, P. Zhou, and L. Liang, “Does environmental regulation affect energy efficiency in China's thermal power generation? Empirical evidence from a slacks-based DEA model,” Energy Policy, Vol. 66, 2014, pp. 537~546. 10.1016/j.enpol.2013.10.056
  9. BP Statistical Review of World Energy, https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html, 2021.
  10. Camarero, M., J. Castillo, A. J. Picazo-Tadeo, and C. Tamarit, “Eco-efficiency and convergence in OECD countries,” Environmental and Resource Economics, Vol. 55, No. 1, 2013, pp. 87~106. 10.1007/s10640-012-9616-9
  11. Camarero, M., A. J. Picazo-Tadeo, and C. “Tamarit, Is the environmental performance of industrialized countries converging? A ‘SURE’ approach to testing for convergence,” Ecological Economics, Vol. 66, No. 4, 2008, pp. 653~661. 10.1016/j.ecolecon.2007.10.024
  12. Chen, J., M. Song, and L. Xu, “Evaluation of environmental efficiency in China using data envelopment analysis,” Ecological Indicators, Vol. 52, 2015, pp, 577~583. 10.1016/j.ecolind.2014.05.008
  13. Chen, L., F. M. Wu, Y. M. Wang, and M. J. Li, “Analysis of the environmental efficiency in China based on the DEA cross‐efficiency approach under different policy objectives,” Expert Systems, Vol. 37, No. 3, 2020, e12461. 10.1111/exsy.12461
  14. Coelli, T., D. P. Rao, and G. E. Battese, Additional Topics in Production Economics, In An Introduction to Efficiency and Productivity Analysis, Springer, 1998, pp. 39~67.10.1007/978-1-4615-5493-6_3
  15. Coelli, T. J., D. S. P. Rao, C. J. O'Donnell, and G. E. Battese, An Introduction to Efficiency and Productivity Analysis, Springer Science & Business Media, 2005.
  16. Coluccia, B., D. Valente, G. Fusco, F. De Leo, and D. Porrini, “Assessing agricultural eco-efficiency in Italian Regions,” Ecological Indicators, Vol. 116, 2020, p. 106483. 10.1016/j.ecolind.2020.106483
  17. Dai, Z., L. Guo, and Z. Jiang, “Study on the industrial Eco-Efficiency in East China based on the Super Efficiency DEA Model: an example of the 2003–2013 panel data,” Applied Economics, Vol. 48, No. 59, 2016, pp. 5779~5785. 10.1080/00036846.2016.1184380
  18. Energy Infromation Administration of U.S. (EIA): https://www.eia.gov/international/data/world/total-energy
  19. Färe, R., S. Grosskopf, and F. Hernandez-Sancho, “Environmental performance: an index number approach,” Resource and Energy Economics, Vol. 26, No. 4, 2004, pp. 343~352. 10.1016/j.reseneeco.2003.10.003
  20. Färe, R., S. Grosskopf, C. K. Lovell, and C. Pasurka, Multilateral productivity comparisons when some outputs are undesirable: a nonparametric approach, The Review of Economics and Statistics, 1989, pp. 90~98. 10.2307/1928055
  21. Färe, R., and D. Primont, Multi-output production and duality: theory and applications, Springer Science & Business Media, 2012.
  22. Feenstra, R. C., R. Inklaar, and M. P. Timmer, “The next generation of the Penn World Table,” American Economic Review, Vol. 105, No. 10, 2015, pp. 3150~3182.10.1257/aer.20130954
  23. Feng, C., M. Wang, G. -C. Liu, and J. -B. Huang, “Green development performance and its influencing factors: A global perspective,” Journal of Cleaner Production, Vol. 144, 2017, pp. 323~333. 10.1016/j.jclepro.2017.01.005
  24. Graham, M., Environmental Efficiency: Meaning and Measurement and Application to Australian Dairy Farms, 2004.
  25. Halkos, G. E., and N. G. Tzeremes, “Exploring the existence of Kuznets curve in countries' environmental efficiency using DEA window analysis,” Ecological Economics, Vol. 68, No. 7, 2009, pp. 2168~2176. 10.1016/j.ecolecon.2009.02.018
  26. Hayami, Y., “Sources of agricultural productivity gap among selected countries,” American Journal of Agricultural Economics, Vol. 51, No. 3, 1969, pp. 564~575. 10.2307/1237909
  27. Hayami, Y., and V. W. Ruttan, “Agricultural productivity differences among countries,” The American Economic Review, Vol. 60, No. 5, 1970, pp. 895~911.
  28. Honma, S., and J. -L. Hu, “A meta-stochastic frontier analysis for energy efficiency of regions in Japan,” Journal of Economic Structures, Vol. 7, No. 1, 2018, pp. 1~16. 10.1186/s40008-018-0119-x
  29. Huang, C. J., T. -H. Huang, and N. -H. Liu, “A new approach to estimating the metafrontier production function based on a stochastic frontier framework,” Journal of Productivity Analysis, Vol. 42, No. 3, 2014, pp. 241~254. 10.1007/s11123-014-0402-2
  30. Koçak, E., H. Kınacı, and K. Shehzad, “Environmental efficiency of disaggregated energy R&D expenditures in OECD: A bootstrap DEA approach,” Environmental Science and Pollution Research, Vol. 28, No. 15, 2021, pp. 19381~19390. 10.1007/s11356-020-12132-w
  31. 33394447
  32. Lansink, A. O., K. Pietola, and S. Bäckman, “Effciency and productivity of conventional and organic farms in Finland 1994–1997,” European Review of Agricultural Economics, Vol. 29, No. 1, 2002, 51~65. 10.1093/erae/29.1.51
  33. Li, H., K. Fang, W. Yang, D. Wang, and X. Hong, “Regional environmental efficiency evaluation in China: Analysis based on the Super-SBM model with undesirable outputs,” Mathematical and Computer Modelling, Vol. 58, No. 5-6, 2013, pp. 1018~1031. 10.1016/j.mcm.2012.09.007
  34. Li, M., and Q. Wang, “International environmental efficiency differences and their determinants,” Energy, Vol. 78, 2014, pp. 411~420. 10.1016/j.energy.2014.10.026
  35. Lin, B., and H. Liu, “CO2 mitigation potential in China's building construction industry: A comparison of energy performance,” Building and Environment, Vol. 94, 2015, pp. 239~251. 10.1016/j.buildenv.2015.08.013
  36. Liu, Q., S. Wang, B. Li, and W. Zhang, “Dynamics, differences, influencing factors of eco-efficiency in China: A spatiotemporal perspective analysis,” Journal of environmental management, Vol. 264, 2020, p. 110442. 10.1016/j.jenvman.2020.110442
  37. 32250887
  38. Liu, Y., J. Zhu, E. Y. Li, Z. Meng, and Y. Song, “Environmental regulation, green technological innovation, and eco-efficiency: The case of Yangtze river economic belt in China,” Technological Forecasting and Social Change, Vol. 155, 2020, p. 119993. 10.1016/j.techfore.2020.119993
  39. Lovell, C. K., P. Travers, S. Richardson, and L. Wood, Resources and functionings: A new view of inequality in Australia, In Models and measurement of welfare and inequality, Springer, 1994, pp. 787~807. 10.1007/978-3-642-79037-9_41
  40. Ma, L. -h., J.-c. Hsieh, and Y. -h. Chiu, “A study on the effects of energy and environmental efficiency at China’s provincial level,” Energies, Vol. 12, No. 4, 2019, p. 591. 10.3390/en12040591
  41. Mavi, R. K., R. F. Saen, and M. Goh, “Joint analysis of eco-efficiency and eco-innovation with common weights in two-stage network DEA: A big data approach,” Technological Forecasting and Social Change, Vol. 144, 2019, pp. 553~562. 10.1016/j.techfore.2018.01.035
  42. Moutinho, V., and M. Madaleno, “Assessing Eco-Efficiency in Asian and African Countries Using Stochastic Frontier Analysis,” Energies, Vol. 14, No. 4, 2021, p. 1168. 10.3390/en14041168
  43. National Institute of Standards and Technology of U.S.(NIST), Special Publication 811(2019), 9th Edition: https://www.nist.gov/pml/special-publication-811/
  44. O’Donnell, C. J., D. P. Rao, and G. E. Battese, “Metafrontier frameworks for the study of firm-level efficiencies and technology ratios,” Empirical Economics, Vol. 34, No. 2, 2008, pp. 231~255. 10.1007/s00181-007-0119-4
  45. Pachauri, R. K., M. R. Allen, V. R. Barros, J. Broome, W. Cramer, R. Christ, J. A. Church, L. Clarke, Q. Dahe, and P. Dasgupta, Climate change 2014: synthesis report. Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. Ipcc. 2014.
  46. Reinhard, S., C. K. Lovell, and G. Thijssen, “Econometric estimation of technical and environmental efficiency: an application to Dutch dairy farms,” American Journal of Agricultural Economics, Vol. 81, No. 1, 1999, pp. 44~60. 10.2307/1244449
  47. Reinhard, S., C. K. Lovell, and G. J. Thijssen, “Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA,” European Journal of Operational Research, Vol. 121, No. 2, 2000, pp. 287~303. 10.1016/S0377-2217(99)00218-0
  48. Ritchie, H., and M. Roser, CO2 and Greenhouse gas Emissions, Our World in Data, 2020.
  49. Robaina-Alves, M., V. Moutinho, and P. Macedo, “A new frontier approach to model the eco-efficiency in European countries,” Journal of Cleaner Production, Vol. 103, 2015, pp. 562~573. 10.1016/j.jclepro.2015.01.038
  50. Schaltegger, S., and A. Sturm, Ökologische rationalität: ansatzpunkte zur ausgestaltung von ökologieorientierten managementinstrumenten, die Unternehmung, 1990, pp. 273~290.
  51. Shukla, P., J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H. Pörtner, D. Roberts, P. Zhai, R. Slade, S. Connors, and R. Van Diemen, IPCC, 2019: Climate Change and Land: an IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems, 2019.
  52. Skevas, T., A. O. Lansink, and S. E. Stefanou, “Measuring technical efficiency in the presence of pesticide spillovers and production uncertainty: The case of Dutch arable farms,” European Journal of Operational Research, Vol. 223, No. 2, 2012, pp. 550~559. 10.1016/j.ejor.2012.06.034
  53. State of Global Air 2020. A Special Report on Global Exposure To Air Pollution And Its Health Impacts, Issue. 2020.
  54. Wang, K., X. Zhang, Y. -M. Wei, and S. Yu, “Regional allocation of CO2 emissions allowance over provinces in China by 2020,” Energy Policy, Vol. 54, 2013, pp. 214~229. 10.1016/j.enpol.2012.11.030
  55. World Bank Databank, Topick: carbon dioxide damage.
  56. World Bank Knowledgebase, https://datahelpdesk.worldbank.org/knowledgebase/articles/378834-how-does-the-world-bank-classify-countries
  57. Wu, J., W. Lu, and M. Li, “A DEA-based improvement of China's green development from the perspective of resource reallocation,” Science of The Total Environment, Vol. 717, 2020, p. 137106. 10.1016/j.scitotenv.2020.137106 32070892
  58. Yang, L., C. Ma, Y. Yang, E. Zhang, and H. Lv, “Estimating the regional eco-efficiency in China based on bootstrapping by-production technologies,” Journal of Cleaner Production, Vol. 243, 2020, p. 118550. 10.1016/j.jclepro.2019.118550
  59. Yang, L., H. Ouyang, K. Fang, L. Ye, and J. Zhang, “Evaluation of regional environmental efficiencies in China based on super-efficiency-DEA,” Ecological Indicators, Vol. 51, 2015, pp. 13~19. 10.1016/j.ecolind.2014.08.040
  60. Yang, L., and X. Zhang, “Assessing regional eco-efficiency from the perspective of resource, environmental and economic performance in China: A bootstrapping approach in global data envelopment analysis,” Journal of Cleaner Production, Vol. 173, 2018, pp. 100~111. 10.1016/j.jclepro.2016.07.166
  61. Yue, W., Z. Liu, M. Su, Z. Gu, and C. Xu, “The impacts of multi-dimension urbanization on energy-environmental efficiency: Empirical evidence from Guangdong Province, China,” Journal of Cleaner Production, Vol. 296, 2021, p. 126513. 10.1016/j.jclepro.2021.126513
  62. Zaim, O., and F. Taskin, “Environmental efficiency in carbon dioxide emissions in the OECD: A nonparametric approach,” Journal of Environmental Management, Vol. 58, No. 2, 2000a, pp. 95~107. 10.1006/jema.1999.0312
  63. Zaim, O., and F. Taskin, “A Kuznets curve in environmental efficiency: an application on OECD countries,” Environmental and Resource Economics, Vol. 17, No. 1, 2000b, pp. 21~36.
  64. Zhang, J., W. Zeng, and H. Shi, “Regional environmental efficiency in China: analysis based on a regional slack-based measure with environmental undesirable outputs,” Ecological Indicators, Vol. 71, 2016, pp. 218~228. 10.1016/j.ecolind.2016.04.040
  65. Zhang, N., and B. Wang, “A deterministic parametric metafrontier Luenberger indicator for measuring environmentally-sensitive productivity growth: a Korean fossil-fuel power case,” Energy Economics, Vol. 51, 2015, pp. 88~98. 10.1016/j.eneco.2015.06.003
  66. Zhou, P., B. Ang, and K. Poh, “Slacks-based efficiency measures for modeling environmental performance,” Ecological Economics, Vol. 60, No. 1, 2006, pp. 111~118. 10.1016/j.ecolecon.2005.12.001
  67. Zhou, P., K. L. Poh, and B. W. Ang, “A non-radial DEA approach to measuring environmental performance,” European Journal of Operational Research, Vol. 178, No. 1, 2007, pp. 1~9.10.1016/j.ejor.2006.04.038
Information
  • Publisher :Korea Resource Economics Association · Korea Environmental Economics Association
  • Publisher(Ko) :한국자원경제학회·한국환경경제학회
  • Journal Title :자원·환경경제연구
  • Journal Title(Ko) :Environmental and Resource Economics Review
  • Volume : 30
  • No :3
  • Pages :471-501