Skip to main content

Advertisement

Log in

Agriculture, forestry, and environmental sustainability: the role of institutions

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

Agriculture and forestry are two primary determinants of the environment, and strong institutions are crucial to moderate the outcomes of these sectors toward a sustainable environment. Therefore, this study aimed to examine the impact of agriculture and forestry on carbon emissions in light of institutional quality. Data at global and five regional levels from 1996 to 2015 were assessed using econometrics tools, namely cross-sectional tests, panel unit root tests, cointegration tests, Driscoll & Kraay, and fully modified ordinary least square regressions and causality analyses. The analysis indicates that agricultural production has a positive effect on CO2 emissions, whereas forestry has a negative impact. The direct and moderating effects of institutional quality on the CO2 emissions are positive. These results emphasize the importance of institutional excellence in the reduction in agricultural and forestry emissions. The study reveals that renewable energy consumption is crucial in improving environmental quality, whereas non-renewable energy consumption is not. Causality analysis reveals bidirectional causality between CO2 emissions and agriculture, forestry, and renewable energy. The study implies that countries should encourage renewable energy and the adoption of environment-friendly practices in agriculture. An increase in forest areas is also important for a clean environment. Nevertheless, the role of institutions for a sustainable environment cannot be underestimated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Abid, M. (2016). Impact of economic, financial, and institutional factors on CO2 emissions: Evidence from Sub-Saharan Africa economies. Utilities Policy, 41, 85–94.

    Article  Google Scholar 

  • Achard, F., Eva, H. D., Mayaux, P., Stibig, H. J., & Belward, A. (2004). Improved estimates of net carbon emissions from land cover change in the tropics for the 1990s. Global Biogeochemical Cycles, 18(2), 1–11.

    Article  CAS  Google Scholar 

  • Adedoyin, F. F., Alola, A. A., & Bekun, F. V. (2020). The nexus of environmental sustainability and agro-economic performance of Sub-Saharan African countries. Heliyon, 6(9), e04878.

  • Agriculture, forestry and other land use (AFOLU) (2014). Climate change: mitigation of climate change. In Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press .

  • Ahillen, S. (2016). Forest fires burn 119,000 acres in 8 Southeastern states. USA Today Networ. Available online https://www.usatoday.com/story/news/nation-now/2016/11/20/forest-fires-burn-119000-acres-8-southeastern-states/94169774/ (Accessed on 10 April 2019).

  • Ahmad, M., Khan, Z., Rahman, Z. U., Khattak, S. I., & Khan, Z. U. (2021). Can innovation shocks determine CO2 emissions (CO2e) in the OECD economies? A new perspective. Economics of Innovation and New Technology, 30(1), 89–109.

    Article  Google Scholar 

  • Apergis, N., Payne, J. E., Menyah, K., & Wolde-Rufael, Y. (2010). On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth. Ecological Economic, 69(11), 2255–2260.

    Article  Google Scholar 

  • Baccini, A. G. S. J., Goetz, S. J., Walker, W. S., Laporte, N. T., Sun, M., Sulla-Menashe, D., & Samanta, S. (2012). Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Climate Change., 2, 182–185.

    Article  CAS  Google Scholar 

  • Balafoutis, A., Beck, B., Fountas, S., Vangeyte, J., Wal, T., Soto, I., & Eory, V. (2017). Precision agriculture technologies positively contributing to GHG emissions mitigation, farm productivity and economics. Sustainability., 9, 1–28.

    Article  Google Scholar 

  • Bauer, J. (2006). International Forest Sector Institutions and Policy Instruments for Europe: A Source Book, p 43, UN.

  • Bélaïd, F., & Youssef, M. (2017). Environmental degradation, renewable and non-renewable electricity consumption, and economic growth: Assessing the evidence from Algeria. Energy Policy, 102, 277–287.

    Article  Google Scholar 

  • Bhattarai, M., & Hammig, M. (2001). Institutions and the environmental Kuznets curve for deforestation: A crosscountry analysis for Latin America. Africa and Asia. World Development, 29(6), 995–1010.

    Google Scholar 

  • Bonduki, Y., & Swisher, J. N. (1995). Options for mitigating greenhouse gas emissions in Venezuela’s forest sector: A general overview. Interciencia, 20(6), 380–387.

    Google Scholar 

  • Brown, S., Dushku, A., Pearson, T., Shoch, D., Winsten, J., Sweet, S., & Kadyszewski, J.(2004). Carbon supply from changes in management of forest, range, and agricultural lands of California. Winrock International for California Energy Commission.

  • Bulut, U. (2017). The impacts of non-renewable and renewable energy on CO 2 emissions in Turkey. Environmental Science and Pollution Research, 24(18), 15416–15426. https://doi.org/10.1007/s11356-017-9175-2

    Article  CAS  Google Scholar 

  • Ching, L. L. (2010). Climate change implications for agriculture in Sub-Saharan Africa. Food and Agriculture Organisation.

    Google Scholar 

  • Das, D., Srinivasan, R., & Sharfuddin, A. (2011). Fossil fuel consumption, carbon emissions and temperature variation in India. Energy & Environment, 22(6), 695–709.

    Article  Google Scholar 

  • Deacon, R. T. (1995). Assessing the relationship between government policy and deforestation. Journal of Environmental Economics and Management, 28(1), 1–18.

    Article  Google Scholar 

  • Dietz, T., & Rosa, E. A. (1994). Rethinking the environmental impacts of population, affluence and technology. Human Ecology Review, 1(2), 277–300.

    Google Scholar 

  • Driscoll, J. C., & Kraay, A. C. (1998). Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics, 80(4), 549–560.

    Article  Google Scholar 

  • Dumitrescu, E. I., & Hurlin, C. (2012). Testing for Granger non-causality in heterogeneous panels. Economic Modelling, 29(4), 1450–2146.

    Article  Google Scholar 

  • Edoja, P. E., Aye, G. C., & Abu, O. (2016). Dynamic relationship among CO2 emission, agricultural productivity and food security in Nigeria. Cogent Economics & Finance, 1(4), 1–13.

    Google Scholar 

  • Gani, A. (2012). The relationship between good governance and carbon dioxide emissions: Evidence from developing economies. Journal of Economic Development., 37(1), 77–93.

    Article  Google Scholar 

  • Hafeez, M., Chunhui, Y., Strohmaier, D., Ahmed, M., & Jie, L. (2018). Does finance affect environmental degradation: evidence from One Belt and One Road Initiative region?. Environmental Science and Pollution Research, pp 1–14. https://doi.org/10.1007/s11356-018-1317-7.

  • Harris, J., & Feriz, M. (2011). Forest, Agriculture, and Climate: Economics and Policy Issues. Tufts University, Medford.

    Google Scholar 

  • Jebli, M. B., & Youssef, S. B. (2017). The role of renewable energy and agriculture in reducing CO2 emissions: Evidence for North Africa countries. Ecological Indicators, 74, 295–301.

    Article  CAS  Google Scholar 

  • Jebli, M. B., & Youssef, S. B. (2018). Renewable energy consumption and agriculture: Evidence for cointegration and Granger causality for Tunisian economy. International Journal of Sustainable Development & World Ecology, 24(2), 149–158.

    Article  Google Scholar 

  • Jebli, M.B., Ben Youssef, S.B., & Ozturk, I. (2013).The environmental Kuznets curve: the role of renewable and non-renewable energy consumption and trade openness. MPRA Paper 51672 University Library of Munich, Germany.

  • Kamal, M. A., Ullah, A., Zheng, J., Zheng, B., & Xia, H. (2019). Natural resource or market seeking motive of China’s FDI in asia? New evidence at income and sub-regional level. Economic Research-Ekonomska Istraživanja, 32(1), 3869–3894.

    Article  Google Scholar 

  • Kang, Y., Khan, S., & Ma, X. (2009). Climate change impacts on crop yield, crop water productivity and food security–A review. Progress in Natural Science, 12, 1665–1674.

    Article  Google Scholar 

  • Kao, C. (1999). Spurious regression and residual-based tests for cointegration in panel. J Economics, 90, 1–44.

    Article  Google Scholar 

  • Kaufmann, D., Kraay, A., & Mastruzzi, M. (2011). The worldwide governance indicators: Methodology and analytical issues. Hague Journal on the Rule of Law, 3(2), 220–246.

    Article  Google Scholar 

  • Khan, A., Chenggang, Y., Hussain, J., & Kui, Z. (2021). Impact of technological innovation, financial development and foreign direct investment on renewable energy, non-renewable energy and the environment in belt & Road Initiative countries. Renewable Energy, 171, 479–491.

    Article  Google Scholar 

  • Liu, X., Zhang, S., & Bae, J. (2017). The impact of renewable energy and agriculture on carbon dioxide emissions: Investigating the environmental Kuznets curve in four selected ASEAN countries. Journal of Cleaner Production, 2017(164), 1239–1247.

    Article  Google Scholar 

  • Martínez-Zarzoso, I., Bengochea-Morancho, A., & Morales-Lage, R. (2007). The impact of population on CO2 emissions: Evidence from European countries. Environmental and Resource Economics, 38(4), 497–512.

    Article  Google Scholar 

  • Maxwell, Sean L., Tom Evans, James EM Watson, Alexandra Morel, Hedley Grantham, Adam Duncan, & Nancy Harris et al.(2019). Degradation and forgone removals increase the carbon impact of intact forest loss by 626%. Science Advances 5, (10) (2019): eaax2546.

  • McEldowney, J. (2020). EPRS| European Parliamentary Research Service. PE 651.922, 2. Available at: https://www.europarl.europa.eu/RegData/etudes/BRIE/2020/651922/EPRS_BRI(2020)651922_EN.pdf

  • McKinley, D. C., Ryan, M. G., Birdsey, R. A., Giardina, C. P., Harmon, M. E., Heath, L. S., & Pataki, D. E. (2011). A synthesis of current knowledge on forests and carbon storage in the United States. Ecological Applications, 6, 1902–1924.

    Article  Google Scholar 

  • Menyah, K., & Wolde-Rufael, Y. (2010). CO2 emissions, nuclear energy, renewable energy and economic growth in the US. Energy Policy, 38(6), 2911–2915.

    Article  CAS  Google Scholar 

  • Mulatu, D. W., Eshete, Z. S., & Gatiso, T. S. (2016) The impact of CO2 emissions on agricultural productivity and household welfare in Ethiopia: A Computable general equilibrium analysis. Environmental Development. Discussion Paper - Resources for the Future (RFF) 2016 No.16-08 pp. 23 pp. ref. 33

  • Nabuurs, G. J., Delacote, P., Ellison, D., Hanewinkel, M., Hetemäki, L., & Lindner, M. (2017). By 2050 the mitigation effects of EU forests could nearly double through climate smart forestry. Forests8(12), 484.

  • Nnaji, C. E., Chukwu, J. O., & Nnaji, M. (2013). Electricity supply, Fossil fuel consumption, Co2 emissions and economic growth: Implications and policy options for sustainable development in Nigeria. International Journal of Energy Economics and Policy, 3(3), 262–271.

    Google Scholar 

  • Padda, I. U. H., & Asim, M. (2019). What determines compliance with cleaner production: An appraisal of tanning industry at Sialkot. Pakistan, Journal of Environmental Science and Pollution Research, 26, 1733–1750.

    Article  Google Scholar 

  • Paluš, H., Parobek, J., Moravčík, M., Kovalčík, M., Dzian, M., & Murgaš, V. (2020). Projecting climate change potential of harvested wood products under different scenarios of wood production and utilization: Study of Slovakia. Sustainability, 12(6), 2510.

    Article  CAS  Google Scholar 

  • Pedroni P, Pedroni P, Peter. (2004). Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Economic Theory 20, 597–625.

  • Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels general diagnostic tests for cross section dependence in panels. Univ.

    Google Scholar 

  • Pesaran, M. H. (2007). A simple panel unit root test in the presence of cross-section dependence. Journal of Applied Econometrics, 22(2), 265–312.

    Article  Google Scholar 

  • Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142(1), 50–93.

    Article  Google Scholar 

  • Phillips, P. C., & Hansen, B. E. (1990). Statistical inference in instrumental variables regression with I (1) processes. The Review of Economic Studies, 57(1), 99–125.

    Article  Google Scholar 

  • Premarathne, W. M. A. G. (2012). The Impact of Informal Institutions on Agricultural Production and Marketing: The Experience of Sri Lanka. 佐賀大学経済論集/佐賀大学経済学会, 45(2), 41–70.

  • Rahman, Z. U., Chongbo, W., & Ahmed, M. (2019). An (a)symmetric analysis of the pollution haven hypothesis in the context of Pakistan: A non-linear approach. Carbon Management. https://doi.org/10.1080/17583004.2019.1577179

    Article  Google Scholar 

  • Rahmann, G. (2011). Biodiversity and organic farming: what do we know?. vTI Agriculture and Forstery Research3, 189–208.

  • Reddy, P. P. (2015). Climate resilient agriculture for ensuring food security (Vol. 373). New Delhi: Springer India.

  • Rizov, M. (2008). Institutions, reform policies and productivity growth in agriculture: Evidence from former communist countries. NJAS-Wageningen Journal of Life Sciences, 55(4), 307–323.

    Article  Google Scholar 

  • Routa, J., Kellomäki, S., Kilpeläinen, A., Peltola, H., & Strandman, H. (2011). Effects of forest management on the carbon dioxide emissions of wood energy in integrated production of timber and energy biomass. GCB Bioenergy, 6, 483–497.

  • Rudel, T. K. (2013). The national determinants of deforestation in sub-Saharan Africa. Philosophical Transactions of the Royal Society b: Biological Sciences, 368(1625), 20120405.

    Article  Google Scholar 

  • Sakurai, Y., Song, K., Tachibana, S., & Takahashi, S. (2014). Volitional enhancement of firing synchrony and oscillation by neuronal operant conditioning: Interaction with neurorehabilitation and brain-machine interface. Frontiers in Systems Neuroscience, 8, 1–11.

    Article  Google Scholar 

  • Salman, M., Long, X., Dauda, L., & Mensah, C. N. (2019). The impact of institutional quality on economic growth and carbon emissions: Evidence from Indonesia, South Korea and Thailand. Journal of Cleaner Production, 241, 118331.

  • Saud, S., Baloch, M. A., & Lodhi, R. N. (2018). The nexus between energy consumption and financial development: estimating the role of globalization in Next-11 countries. Environmental Science and Pollution Research, 1–11.

  • OECD Science, Technology and Innovation Outlook 2021: https://doi.org/10.1787/25186167. Available at https://www.oecd-ilibrary.org/science-and-technology/oecd-science-technology-and-innovationoutlook_25186167.

  • Shah, W. U. H., Yasmeen, R., & Padda, I. U. H. (2019). An analysis between financial development, institutions, and the environment: a global view. Environmental Science and Pollution Research, 1–13. https://doi.org/10.1007/s11356-019-05450-1.

  • Smith, P., Martino, D., Cai, Z., Gwary, D., & Janzen, H., et al. (2007a) Climate Change 2007: Mitigation. In Metz, B., Davidson, O. R., Bosch, P. R., Dave, R., Meyer, L. A. (eds.). Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.

  • Stern, N., Peters, S., Bakhshi, V., Bowen, A., Cameron, C., Catovsky, S., & Edmonson, N. (2006). Stern Review: The economics of climate change. HM treasury.

    Google Scholar 

  • Suo, X., & Cao, S. (2021). China’s three north shelter forest program: Cost–benefit analysis and policy implications. Environment, Development and Sustainability, 23, 14605–14618. https://doi.org/10.1007/s10668-021-01260-z

    Article  Google Scholar 

  • The world bank report: The Changing Wealth of Nations (2018) available at: https://www.worldbank.org/en/news/feature/2018/01/30/the-changing-wealth-of-nations-2018

  • Varoudakis, A., Tiongson, E. R., & Pushak, T. (2007). Public finance, governance, and growth in transition economies: Empirical evidence from 1992–2004. The World Bank.

  • Waheed, R., Chang, D., Sarwar, S., & Chen, W. (2018). Forest, agriculture, renewable energy, and CO2 emission. Journal of Cleaner Production, 172, 4231–4238.

    Article  Google Scholar 

  • Wang, M., Arshed, N., Munir, M., et al. (2021). Investigation of the STIRPAT model of environmental quality: A case of nonlinear quantile panel data analysis. Environment, Development and Sustainability, 23, 12217–12232. https://doi.org/10.1007/s10668-020-01165-3

    Article  Google Scholar 

  • Wegren, S. K. (2012). Institutional impact and agricultural change in Russia. Journal of Eurasian Studies, (2), 193–202.

  • Westerlund, J. (2007). Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics, 69, 709–748.

    Article  Google Scholar 

  • Wolfslehner, B., Pülzl, H., Kleinschmit, D., Aggestam, F., Winkel, G., Candel, J., Eckerberg, J., Feindt, P., McDermott, C., Secco, L., Sotirov, M., Lackner, M., & Roux, J. L. (2020). European forest governance post-2020. European Forest Institute. https://efi.int/sites/default/files/files/publication-bank/2020/efi_fstp_10_2020.pdf

  • World Development Indicators (2017). https://databank.worldbank.org/data/source/world-development-indicators.

  • Worldwide Governance Indicators (2017). https://databank.worldbank.org/source/worldwide-governance indicators.

  • Yao, X., Yasmeen, R., Hussain, J., & Shah, W. U. H. (2021). The repercussions of financial development and corruption on energy efficiency and ecological footprint: Evidence from BRICS and next 11 countries. Energy223, 120063.

  • Yasmeen, R., Li, Y., Hafeez, M., & Ahmad, H. (2018). The trade-environment nexus in light of governance: a global potential. Environmental Science and Pollution Research, pp. 1–20.

  • Yasmeen, R., Li, Y., and Hafeez, M. Tracing the trade–pollution nexus in global value chains: evidence from air pollution indicators. Environmental Science and Pollution Research, 2019. 1–13. https://doi.org/10.1007/s11356-018-3956-0.

  • Zylbersztajn, D., & Center, P. A. K. (2009). Role of institutions in reshaping the global agricultural landscape: perspectives from Brazil. International Association of Agricultural Economists, Beijing, China.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ihtsham Ul Haq Padda.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Tables 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, and 19.

Table 6 Summary of the empirical literature (Agriculture, Forest, energy, institutions, and environment)
Table 7 Country list
Table 8 Data description
Table 9 Descriptive analysis
Table 10 Test of slope homogeneity
Table 11 CD-T and CIPS test
Table 12 Cointegration association (Agriculture—model)
Table 13 Cointegration association (Forest—model)
Table 14 Cointegration association (Agriculture & Institutions– model)
Table 15 Cointegration association (Forest & Institutions– model)
Table 16 Agriculture Model (FMOLS)
Table 17 Forest model (FMOLS)
Table 18 Agriculture & Institutions Model (FMOLS)
Table 19 Forest & Institutions Model (FMOLS)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yasmeen, R., Padda, I.U.H., Yao, X. et al. Agriculture, forestry, and environmental sustainability: the role of institutions. Environ Dev Sustain 24, 8722–8746 (2022). https://doi.org/10.1007/s10668-021-01806-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10668-021-01806-1

Keywords

Navigation