Environmental Science and Pollution Research

, Volume 23, Issue 11, pp 10968–10982 | Cite as

The relationship between carbon dioxide and agriculture in Ghana: a comparison of VECM and ARDL model

Research Article

Abstract

In this paper, the relationship between carbon dioxide and agriculture in Ghana was investigated by comparing a Vector Error Correction Model (VECM) and Autoregressive Distributed Lag (ARDL) Model. Ten study variables spanning from 1961 to 2012 were employed from the Food Agricultural Organization. Results from the study show that carbon dioxide emissions affect the percentage annual change of agricultural area, coarse grain production, cocoa bean production, fruit production, vegetable production, and the total livestock per hectare of the agricultural area. The vector error correction model and the autoregressive distributed lag model show evidence of a causal relationship between carbon dioxide emissions and agriculture; however, the relationship decreases periodically which may die over-time. All the endogenous variables except total primary vegetable production lead to carbon dioxide emissions, which may be due to poor agricultural practices to meet the growing food demand in Ghana. The autoregressive distributed lag bounds test shows evidence of a long-run equilibrium relationship between the percentage annual change of agricultural area, cocoa bean production, total livestock per hectare of agricultural area, total pulses production, total primary vegetable production, and carbon dioxide emissions. It is important to end hunger and ensure people have access to safe and nutritious food, especially the poor, orphans, pregnant women, and children under-5 years in order to reduce maternal and infant mortalities. Nevertheless, it is also important that the Government of Ghana institutes agricultural policies that focus on promoting a sustainable agriculture using environmental friendly agricultural practices. The study recommends an integration of climate change measures into Ghana’s national strategies, policies and planning in order to strengthen the country’s effort to achieving a sustainable environment.

Keywords

Carbon dioxide Agricultural emissions Cointegration VECM ARDL Ghana 

Acronyms

AIC

Akaike Information Criterion

ARDL

Autoregressive Distributed Lag

CHAL

% annual change (Agricultural area)

COAGRAPROD

Coarse Grain, Production (Tons)

COCOBE

Cocoa, beans Production (Tonnes)

FPE

Final Prediction Error

FRUPROD

Fruit excl Melons, Production (Tons)

HQ

Hannan-Quinn Information Criteria

LIVEHEC

Livestock total per hectare of agricultural area (No/Ha)

LR

Sequential Likelihood-Ratio

MDGs

Millennium Development Goals

PULPROD

Total Pulses Production

RNTPROD

Roots and Tubers, Total Production (Tons)

SC

Schwarz Information Criterion

SDG

Sustainable Development Goal

VAR

Vector Autoregression

VECM

Vector Error Correction Model

VEGPROD

Vegetable Production (Tons)

Abbreviations

Chi2

Chi square

Coef.

Coefficient

cointEq

Cointegrated equation

df

Difference

Prob

Probability

Std. Err.

Standard error

Greek Letter

π

Rank

JEL Classification

Q54 Q57 

References

  1. Adom PK, Bekoe W (2012) Conditional dynamic forecast of electrical energy consumption requirements in Ghana by 2020: a comparison of ARDL and PAM. Energy 44:367–380CrossRefGoogle Scholar
  2. Asumadu-Sarkodie S, Owusu PA (2015) Media impact on students’ body image. Int J Res Appl Sci Eng Technol 3:460–469Google Scholar
  3. Asumadu-Sarkodie S, Owusu P (2016a) Feasibility of biomass heating system in Middle East Technical University, Northern Cyprus campus cogent engineering doi:10.1080/23311916.2015.1134304
  4. Asumadu-Sarkodie S, Owusu PA (2016b). The potential and economic viability of solar photovoltaic in Ghana. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. doi:10.1080/15567036.2015.1122682
  5. Asumadu-Sarkodie S, Owusu PA (2016c). The potential and economic viability of wind farm in Ghana. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. doi:10.1080/15567036.2015.1122680
  6. Asumadu-Sarkodie, S., & Owusu, P. A. (2016d). Multivariate Co-integration Analysis of the Kaya Factors In Ghana. Environmental Science and Pollution Research. doi:10.1007/s11356-016-6245-9
  7. Asumadu-Sarkodie S, Owusu PA, Jayaweera HM (2015a) Flood risk management in Ghana: a case study in Accra. Adv Appl Sci Res 6:196–201Google Scholar
  8. Asumadu-Sarkodie S, Owusu PA, Rufangura P (2015b) Impact analysis of flood in Accra, Ghana. Adv Appl Sci Res 6:53–78Google Scholar
  9. Bakhtiari AA, Hematian A, Sharifi A (2015) Energy analyses and greenhouse gas emissions assessment for saffron production cycle. Environ Sci Pollut Res Int 22:16184–16201. doi:10.1007/s11356-015-4843-6 CrossRefGoogle Scholar
  10. Borah L, Baruah KK (2015) Nitrous oxide emission and mitigation from wheat agriculture: association of physiological and anatomical characteristics of wheat genotypes. Environ Sci Pollut Res Int. doi:10.1007/s11356-015-5299-4 Google Scholar
  11. Breitung J (1999) The local power of some unit root tests for panel data. Discussion papers, interdisciplinary research project 373: quantification and simulation of economic processesGoogle Scholar
  12. Brown RL, Durbin J, Evans JM (1975) Techniques for testing the constancy of regression relationships over time. J R Stat Soc Ser B (Methodol):149–192Google Scholar
  13. Burney JA, Davis SJ, Lobell DB (2010) Greenhouse gas mitigation by agricultural intensification. Proc Natl Acad Sci 107:12052–12057CrossRefGoogle Scholar
  14. Busch J et al (2012) Structuring economic incentives to reduce emissions from deforestation within Indonesia. Proc Natl Acad Sci U S A 109:1062–1067CrossRefGoogle Scholar
  15. Chang C-C (2010) A multivariate causality test of carbon dioxide emissions, energy consumption and economic growth in China. Appl Energy 87:3533–3537CrossRefGoogle Scholar
  16. Chang SJ (2013) Solving the problem of carbon dioxide emissions. For Policy Econ 35:92–97. doi:10.1016/j.forpol.2013.06.013 CrossRefGoogle Scholar
  17. Choi I (2001) Unit root tests for panel data. J Int Money Financ 20:249–272CrossRefGoogle Scholar
  18. Earth System Research Laboratory (2015) The NOAA Annual Greenhouse Gas Index (AGGI). http://www.esrl.noaa.gov/gmd/aggi/aggi.html. Accessed Oct 24, 2015
  19. Engle RF, Granger CW (1987) Co-integration and error correction: representation, estimation, and testing Econometrica: J Econ Soci 251–276Google Scholar
  20. Farhani S, Ozturk I (2015) Causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia. Environ Sci Pollut Res Int 22:15663–15676. doi:10.1007/s11356-015-4767-1 CrossRefGoogle Scholar
  21. Fei L, Dong S, Xue L, Liang Q, Yang W (2011) Energy consumption-economic growth relationship and carbon dioxide emissions in China. Energ Policy 39:568–574. doi:10.1016/j.enpol.2010.10.025 CrossRefGoogle Scholar
  22. Food Agriculture Organization FAO Statistical Yearbooks—World food and agriculture. http://faostat3.fao.org/home/E
  23. Fuinhas JA, Marques AC (2012) Energy consumption and economic growth nexus in Portugal, Italy, Greece, Spain and Turkey: an ARDL bounds test approach (1965–2009). Energy Econ 34:511–517. doi:10.1016/j.eneco.2011.10.003 CrossRefGoogle Scholar
  24. Granger CW (1988) Some recent development in a concept of causality. J Econ 39:199–211CrossRefGoogle Scholar
  25. Gul S, Zou X, Hassan CH, Azam M, Zaman K (2015) Causal nexus between energy consumption and carbon dioxide emission for Malaysia using maximum entropy bootstrap approach. Environ Sci Pollut Res 1–13Google Scholar
  26. Hadri K (2000) Testing for stationarity in heterogeneous panel data. Econ J 148–161Google Scholar
  27. Hagemann M, Ndambi A, Hemme T, Latacz-Lohmann U (2012) Contribution of milk production to global greenhouse gas emissions. An estimation based on typical farms. Environ Sci Pollut Res Int 19:390–402. doi:10.1007/s11356-011-0571-8 CrossRefGoogle Scholar
  28. Huang B-N, Hwang MJ, Yang CW (2008) Causal relationship between energy consumption and GDP growth revisited: a dynamic panel data approach. Ecol Econ 67:41–54. doi:10.1016/j.ecolecon.2007.11.006 CrossRefGoogle Scholar
  29. Hussain S, Peng S, Fahad S, Khaliq A, Huang J, Cui K, Nie L (2015) Rice management interventions to mitigate greenhouse gas emissions: a review. Environ Sci Pollut Res Int 22:3342–3360. doi:10.1007/s11356-014-3760-4 CrossRefGoogle Scholar
  30. Im KS, Pesaran MH, Shin Y (2003) Testing for unit roots in heterogeneous panels. J Econ 115:53–74CrossRefGoogle Scholar
  31. Johansen S (1995) Likelihood-based inference in cointegrated vector autoregressive models OUP catalogueGoogle Scholar
  32. Kankal M, Akpınar A, Kömürcü Mİ, Özşahin TŞ (2011) Modeling and forecasting of Turkey’s energy consumption using socio-economic and demographic variables. Appl Energy 88:1927–1939. doi:10.1016/j.apenergy.2010.12.005 CrossRefGoogle Scholar
  33. Levin A, Lin C-F, Chu C-SJ (2002) Unit root tests in panel data: asymptotic and finite-sample properties. J Econ 108:1–24CrossRefGoogle Scholar
  34. Li W, Ou Q, Chen Y (2014) Decomposition of China’s CO2 emissions from agriculture utilizing an improved Kaya identity. Environ Sci Pollut Res Int 21:13000–13006. doi:10.1007/s11356-014-3250-8 CrossRefGoogle Scholar
  35. Liu W et al (2015) Greenhouse gas emissions, soil quality, and crop productivity from a mono-rice cultivation system as influenced by fallow season straw management. Environ Sci Pollut Res IntGoogle Scholar
  36. Lozano S, Gutiérrez E (2008) Non-parametric frontier approach to modelling the relationships among population, GDP, energy consumption and CO2 emissions. Ecol Econ 66:687–699. doi:10.1016/j.ecolecon.2007.11.003 CrossRefGoogle Scholar
  37. Maddala GS, Wu S (1999) A comparative study of unit root tests with panel data and a new simple test. Oxf Bull Econ Stat 61:631–652CrossRefGoogle Scholar
  38. Mahadeva L, Robinson P (2004) Unit root testing to help model buildingGoogle Scholar
  39. Ministry of Food and Agriculture Agriculture in Ghana, Facts and FiguresGoogle Scholar
  40. Ozturk I, Acaravci A (2011) Electricity consumption and real GDP causality nexus: evidence from ARDL bounds testing approach for 11 MENA countries. Appl Energy 88:2885–2892CrossRefGoogle Scholar
  41. Pesaran MH, Shin Y (1998) An autoregressive distributed-lag modelling approach to cointegration analysis. Econ Soc Monogr 31:371–413Google Scholar
  42. Pesaran MH, Shin Y, Smith RJ (2001) Bounds testing approaches to the analysis of level relationships. J Appl Econ 16:289–326CrossRefGoogle Scholar
  43. Roth E et al (2014) Impact of raw pig slurry and pig farming practices on physicochemical parameters and on atmospheric N2O and CH4 emissions of tropical soils, Uvea Island (South Pacific). Environ Sci Pollut Res Int 21:10022–10035. doi:10.1007/s11356-014-3048-8 CrossRefGoogle Scholar
  44. Soytas U, Sari R (2009) Energy consumption, economic growth, and carbon emissions: challenges faced by an EU candidate member. Ecol Econ 68:1667–1675. doi:10.1016/j.ecolecon.2007.06.014 CrossRefGoogle Scholar
  45. Tilman D, Cassman KG, Matson PA, Naylor R, Polasky S (2002) Agricultural sustainability and intensive production practices. Nature 418:671–677CrossRefGoogle Scholar
  46. UNTC United Nations Treaty Collection https://treaties.un.org/. Accessed 14 Nov 2015
  47. Zhang X-P, Cheng X-M (2009) Energy consumption, carbon emissions, and economic growth in China. Ecol Econ 68:2706–2712. doi:10.1016/j.ecolecon.2009.05.011 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Samuel Asumadu-Sarkodie
    • 1
  • Phebe Asantewaa Owusu
    • 1
  1. 1.Sustainable Environment and Energy SystemsMiddle East Technical University, Northern Cyprus CampusGuzelyurtTurkey

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