Advertisement

Regional spatial patterns and influencing factors of environmental auditing for sustainable development: summaries and illuminations from international experiences

  • Haiyan Lu
  • Yanqiang WeiEmail author
  • Suchang Yang
  • Yunwei Liu
Article
  • 42 Downloads

Abstract

Environmental auditing (EA) is an efficient tool for supervising environmental governance for the realization of economic and social sustainable development. However, the extent to which EA influences the implementation of environmental protection projects and the dominating socioeconomic factors affecting the implementation of EA are unclear. Due to limited data availability, reports investigating this issue are relatively scarce. Based on annual investigation data from the International Organisation of Supreme Audit Institutions regarding the implementation of world EA projects between 2003 and 2013, this paper performed exploratory spatial data analysis to analyze the spatial correlations of EA projects in 204 countries/regions worldwide. In addition, spatial regression models for the year 2013 were established. The findings suggest that (I) high–high regions were mainly concentrated in North America, Europe, and parts of Latin America. The local implementation of EA had strong spatial interactive effects and was heavily influenced by adjacent regions. An experimental area that has already implemented EA can influence its adjacent regions, and the surrounding regions may learn from such implementation. (II) The spatial regression coefficient of the spatial lag model was 0.323 and highly significant (p < 0.001) compared to the traditional regression model. After considering the spatial variables, the importance of CO2 emissions decreased by 1.7%, while environmental input and per capita income decreased by 0.7% and 1%, respectively. The influence of these factors on the implementation of EA was driven by the factor of spatial proximity, which was a critical factor influencing the results. (III) In general, socioeconomic development level, environmental protection investment, and other factors, such as CO2 emission level and degree of social informatization, all increased the efficiency of EA implementation. These conclusions emphasize the important role of EA in helping local governments with environmental management and sustainable development.

Keywords

Spatial effect Exploratory spatial data analysis (ESDA) Spatial regression model Environmental auditing Environmental management Sustainable development 

Notes

Acknowledgements

We are grateful to two anonymous reviewers who contribute in ameliorating the original version of this manuscript in the peer review of this work. This research was jointly supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA19040502), the National Natural Science Foundation of China (Grant No. 41701505), and the Major Program of National Natural Science Foundation of China (Grant No. 41690143).

Compliance with ethical standards

Conflict of interests

The authors declare that they have no conflict of interests.

References

  1. Agovino, M., Ferrara, M., & Garofalo, A. (2016). An exploratory analysis on waste management in Italy: A focus on waste disposed in landfill. Land Use Policy, 57, 669–681.  https://doi.org/10.1016/j.landusepol.2016.06.027.CrossRefGoogle Scholar
  2. Alvarez-Larrauri, R., & Fogel, I. (2008). Environmental audits as a policy of state: 10 years of experience in Mexico. Journal of Cleaner Production, 16(1), 66–74.  https://doi.org/10.1016/j.jclepro.2006.11.006.CrossRefGoogle Scholar
  3. Anselin, L. (1995). Local Indicators of Spatial Association—LISA. Geographical Analysis, 27(2), 93–115.  https://doi.org/10.1111/j.1538-4632.1995.tb00338.x.CrossRefGoogle Scholar
  4. Anselin, L. (2010). Thirty years of spatial econometrics. Papers in Regional Science, 89(1), 3–25.  https://doi.org/10.1111/j.1435-5957.2010.00279.x.CrossRefGoogle Scholar
  5. Anselin, L., Sridharan, S., & Gholston, S. (2007). Using exploratory spatial data analysis to leverage social indicator databases: The discovery of interesting patterns. Social Indicators Research, 82(2), 287–309.  https://doi.org/10.1007/s11205-006-9034-x.CrossRefGoogle Scholar
  6. Behrens, K., Ertur, C., & Koch, W. (2012). Dual gravity: Using spatial econometrics to control for multilateral resistance. Journal of Applied Econometrics, 27(5), 773–794.  https://doi.org/10.1002/jae.1231.CrossRefGoogle Scholar
  7. Celebioglu, F., & Dall’erba, S. (2010). Spatial disparities across the regions of Turkey: An exploratory spatial data analysis. The Annals of Regional Science, 45(2), 379–400.  https://doi.org/10.1007/s00168-009-0313-8.CrossRefGoogle Scholar
  8. Chen, J. D., Xu, C., Li, K., & Song, M. L. (2018). A gravity model and exploratory spatial data analysis of prefecture-scale pollutant and CO2 emissions in China. Ecological Indicators, 90, 554–563.  https://doi.org/10.1016/j.ecolind.2018.03.057.CrossRefGoogle Scholar
  9. Cook, W., van Bommel, S., & Turnhout, E. (2016). Inside environmental auditing: Effectiveness, objectivity, and transparency. Current Opinion in Environmental Sustainability, 18, 33–39.  https://doi.org/10.1016/j.cosust.2015.07.016.CrossRefGoogle Scholar
  10. Djukpen, R. O. (2012). Mapping the HIV/AIDS epidemic in Nigeria using exploratory spatial data analysis. GeoJournal, 77(4), 555–569.  https://doi.org/10.1007/s10708-010-9350-1.CrossRefGoogle Scholar
  11. Dou, Y., Luo, X., Dong, L., Wu, C. T., Liang, H. W., & Ren, J. Z. (2016). An empirical study on transit-oriented low-carbon urban land use planning: Exploratory spatial data analysis (ESDA) on Shanghai, China. Habitat International, 53, 379–389.  https://doi.org/10.1016/j.habitatint.2015.12.005.CrossRefGoogle Scholar
  12. Gao, X. L., Asami, Y., & Chung, C.-J. F. (2006). An empirical evaluation of spatial regression models. Computers & Geosciences, 32(8), 1040–1051.  https://doi.org/10.1016/j.cageo.2006.02.010.CrossRefGoogle Scholar
  13. Gao, L., & Bryan, B. A. (2017). Finding pathways to national-scale land-sector sustainability. Nature, 544, 217.  https://doi.org/10.1038/nature21694.CrossRefGoogle Scholar
  14. Geniaux, G., & Martinetti, D. (2018). A new method for dealing simultaneously with spatial autocorrelation and spatial heterogeneity in regression models. Regional Science and Urban Economics, 72, 74–85.  https://doi.org/10.1016/j.regsciurbeco.2017.04.001.CrossRefGoogle Scholar
  15. Gray, R. (2000). Current developments and trends in social and environmental auditing, reporting and attestation: A review and comment. International Journal of Auditing, 4(3), 247–268.  https://doi.org/10.1111/1099-1123.00316.CrossRefGoogle Scholar
  16. Grubesic, T. H., & Mack, E. A. (2008). Spatio-temporal interaction of urban crime. Journal of Quantitative Criminology, 24(3), 285–306.  https://doi.org/10.1007/s10940-008-9047-5.CrossRefGoogle Scholar
  17. Haining, R. (1993). Spatial data analysis in the social and environmental sciences. London: Cambridge University Press.Google Scholar
  18. Hák, T., Janoušková, S., & Moldan, B. (2016). Sustainable development goals: A need for relevant indicators. Ecological Indicators, 60, 565–573.  https://doi.org/10.1016/j.ecolind.2015.08.003.CrossRefGoogle Scholar
  19. Huang, R. B. (2011). Environmental auditing: An informationized regulatory tool of carbon emission reduction. Energy Procedia, 5, 6–14.  https://doi.org/10.1016/j.egypro.2011.03.002.CrossRefGoogle Scholar
  20. Huang, R. B. (2013). An empirical analysis of the influencing factors on the environmental auditing system choice. China Population, Resources and Environment, 23(10), 134–140. (In Chinese with English abstract).Google Scholar
  21. INTOSAI (2007). Evolution and trends in environmental auditing. In INTOSAI working group on environmental auditing.Google Scholar
  22. INTOSAI (2012). The 7th survey on environmental auditing. In INTOSAI working group on environmental auditing.Google Scholar
  23. INTOSAI (2016). The 8th survey on environmental auditing. INTOSAI working group on environmental auditing.Google Scholar
  24. Kluczek, A., & Olszewski, P. (2017). Energy audits in industrial processes. Journal of Cleaner Production, 142, 3437–3453.  https://doi.org/10.1016/j.jclepro.2016.10.123.CrossRefGoogle Scholar
  25. Leeuwen, S. V. (2004). Developments in environmental auditing by supreme audit institutions. Environmental Management, 33(2), 163–172.  https://doi.org/10.1007/s00267-003-0063-9.CrossRefGoogle Scholar
  26. Li, X., & Griffin, W. A. (2013). Using ESDA with social weights to analyze spatial and social patterns of preschool children’s behavior. Applied Geography, 43, 67–80.  https://doi.org/10.1016/j.apgeog.2013.06.003.CrossRefGoogle Scholar
  27. Li, L., & Zhang, L. P. (2012). The survey results of global environmental audit conducted by WGEA: Analysis and implications. Auditing Research, 1, 33–39. (In Chinese with English abstract).Google Scholar
  28. Lincaru, C., Atanasiu, D., Ciucă, V., & Pirciog, S. (2016). Peri-urban areas and land use structure in Romania at LAU2 level: An exploratory spatial data analysis. Procedia Environmental Sciences, 32, 124–137.  https://doi.org/10.1016/j.proenv.2016.03.017.CrossRefGoogle Scholar
  29. Lundqvist, L. J. (2001). Implementation from above: The ecology of power in Sweden’s environmental governance. Governance, 14(3), 319–337.  https://doi.org/10.1111/0952-1895.00163.CrossRefGoogle Scholar
  30. Ma, X. Y., & Pei, T. (2012). Exploratory spatial data analysis of regional economic disparities in Beijing during 2001–2007. In A. G. O. Yeh, W. Shi, Y. Leung, & C. Zhou (Eds.), Advances in spatial data handling and GIS, Berlin, Heidelberg (pp. 39–48). Berlin: Springer.CrossRefGoogle Scholar
  31. Meng, B., Wang, J. G., Andrew, R., Xiao, H., Xue, J. J., & Peters, G. P. (2017). Spatial spillover effects in determining China’s regional CO2 emissions growth: 2007–2010. Energy Economics, 63, 161–173.  https://doi.org/10.1016/j.eneco.2017.02.001.CrossRefGoogle Scholar
  32. Mikulčić, H., Duić, N., & Dewil, R. (2017). Environmental management as a pillar for sustainable development. Journal of Environmental Management, 203, 867–871.  https://doi.org/10.1016/j.jenvman.2017.09.040.Google Scholar
  33. Moran, P. (1953). The statistical analysis of the Canadian Lynx cycle. Australian Journal of Zoology, 1(3), 291–298.  https://doi.org/10.1071/ZO9530291.CrossRefGoogle Scholar
  34. Oom, D., & Pereira, J. M. C. (2013). Exploratory spatial data analysis of global MODIS active fire data. International Journal of Applied Earth Observation and Geoinformation, 21, 326–340.  https://doi.org/10.1016/j.jag.2012.07.018.CrossRefGoogle Scholar
  35. Owusu-Edusei, K., & Owens, C. J. (2009). Monitoring county-level chlamydia incidence in Texas, 2004–2005: Application of empirical Bayesian smoothing and Exploratory Spatial Data Analysis (ESDA) methods. International Journal of Health Geographics, 8(1), 12.  https://doi.org/10.1186/1476-072X-8-12.CrossRefGoogle Scholar
  36. Qian, W., Hörisch, J., & Schaltegger, S. (2018). Environmental management accounting and its effects on carbon management and disclosure quality. Journal of Cleaner Production, 174, 1608–1619.  https://doi.org/10.1016/j.jclepro.2017.11.092.CrossRefGoogle Scholar
  37. Ruban, A., & Rydén, L. (2019). Introducing environmental auditing as a tool of environmental governance in Ukraine. Journal of Cleaner Production, 212, 505–514.  https://doi.org/10.1016/j.jclepro.2018.11.059.CrossRefGoogle Scholar
  38. Sachs, J. D. (2012). From millennium development goals to sustainable development goals. The Lancet, 379(9832), 2206–2211.  https://doi.org/10.1016/S0140-6736(12)60685-0.CrossRefGoogle Scholar
  39. Schaltegger, S., & Roger, B. (2017). Contemporary environmental accounting: Issues, concepts and practice. Abingdon: Routledge.CrossRefGoogle Scholar
  40. Sinclair-Desgagné, B., & Gabel, H. L. (1997). Environmental auditing in management systems and public policy. Journal of Environmental Economics and Management, 33(3), 331–346.  https://doi.org/10.1006/jeem.1997.0993.CrossRefGoogle Scholar
  41. Stafford, S. L. (2006). State adoption of environmental audit initiatives. Contemporary Economic Policy, 24(1), 172–187.  https://doi.org/10.1093/cep/byj010.CrossRefGoogle Scholar
  42. UN. (1982). A world charter for nature. New York: United Nations.Google Scholar
  43. UN (2015a). The millennium development goals report 2015. New York.Google Scholar
  44. UN. (2015b). Transforming our world: The 2030 Agenda for Sustainable Development. New York: United Nations.Google Scholar
  45. Wilson, B., & Greenlee, A. J. (2016). The geography of opportunity: An exploratory spatial data analysis of US counties. GeoJournal, 81(4), 625–640.  https://doi.org/10.1007/s10708-015-9642-6.CrossRefGoogle Scholar
  46. Wu, J. Y., Mao, Y. L., & Gao, Y. C. (2003). The theoretical basis of environmental auditing. Environmental Science Trends, 3, 22–23. (In Chinese with English abstract).Google Scholar
  47. Ye, X. Y., & Wu, L. (2011). Analyzing the dynamics of homicide patterns in Chicago: ESDA and spatial panel approaches. Applied Geography, 31(2), 800–807.  https://doi.org/10.1016/j.apgeog.2010.08.006.CrossRefGoogle Scholar
  48. Zhang, X. Y., & Yu, J. H. (2018). Spatial weights matrix selection and model averaging for spatial autoregressive models. Journal of Econometrics, 203(1), 1–18.  https://doi.org/10.1016/j.jeconom.2017.05.021.CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of EconomicsLanzhou UniversityLanzhouPeople’s Republic of China
  2. 2.School of AccountancyLanzhou University of Finance and EconomicsLanzhouPeople’s Republic of China
  3. 3.Key Laboratory of Remote Sensing of Gansu ProvinceNorthwest Institute of Eco-Environment and Resources, Chinese Academy of SciencesLanzhouPeople’s Republic of China
  4. 4.School of Environment and ResourceXichang UniversityXichangPeople’s Republic of China

Personalised recommendations