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Interface between energy consumption, CO2 emissions, economic growth, and macroeconomic openness in financial action task force countries through the lens of a causality approach

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Abstract

There is strong scientific evidence to suggest that carbon dioxide (CO2) emissions are one of the key drivers of global warming. Rising CO2 emissions across the globe have been traced back to increasing global trade and rapid industrial development powered by fossil fuels. High CO2 emissions have had an adverse effect on the quality of life and economic growth of communities across the globe. In this study, the Granger causality approach is used to examine scientifically some causal relationships between energy consumption, CO2 emissions, economic growth, and key macroeconomic variables (trade openness and foreign direct investment) in the panel of Financial Action Task Force (FATF) countries. FATF countries are signatories to agreements to adhere to good financial practices to ensure sustainable development of their economies. The empirical analysis was conducted for the period 1980 to 2020. Results indicate a strong endogenous relationship between the variables in the short and long run. The analysis suggests that careful co-curation of economic, trade, energy, foreign direct investment, and environmental management policies is needed to ensure sustainable economic development in the FATF countries. Global trade and foreign direct investment policies must foster new environmental-friendly industries and greater use of clean renewable energy among these countries.

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Note: Arrows indicate direction of possible causal links between the variables.

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Notes

  1. This is one of the most severe challenges faced throughout the world today. Consequently, the phenomenon is studied and analysed by academics, environmentalists, and policymakers. Climate change has been attributed to greenhouse gases, to which carbon emissions contribute significantly (see, for instance, Uzar 2020; Chien et al. 2021; Sun et al. 2021; Sun et al. 2022; Sohail et al. 2022; Wangzhou et al. 2022).

  2. The FATF includes various jurisdictions and regional organisations (the Gulf Cooperation Council and the European Commission). The countries included in our analysis are Argentina, Australia, Austria, Belgium, Brazil, Canada, China, Denmark, Finland, France, Germany, Greece, Hong Kong, Iceland, India, Ireland, Italy, Japan, the Republic of Korea, Luxembourg, Malaysia, Mexico, the Netherlands, New Zealand, Norway, Portugal, the Russian Federation, Singapore, South Africa, Spain, Sweden, Switzerland, Turkey, the UK, and the USA.

  3. The use of more efficient technologies may be one reason that some studies do not find the inverted U-curve.

  4. Rapid socio-economic development can increase the demand for energy, which in turn raises CO2 emissions (Yu et al. 2014).

  5. The studies mentioned appear to indicate a strong positive nexus between energy consumption and CO2. Their results support the argument that cities with high energy consumption have higher carbon emissions (see, for example, Salahuddin and Gow, 2014). When cities have high carbon emissions, governments react by attempting to implement regulatory measures to discourage fuel consumption and reduce carbon emissions. One such measure is to impose strong restrictions on energy consumption. Such policies are implemented by various means, for example, by raising the price of fossil fuels (natural gas, coal, and oil), imposing tolls on private vehicles (particularly, through high tolls during business hours), encouraging public transport, and introducing alternates such as electrical vehicles. Governments may not impose restrictions on energy consumption or implement any of these policies if a country does not have unacceptably high carbon emissions. This then implies strong causality between energy consumption and emissions, leading to significant consequences for and actions in an economy.

  6. Trade openness (OPE) refers to the ratio between imports and exports (exports + imports) over real GDP. This measure captures a country’s trade flows. There are some limitations to using this measure to capture trade openness, but the other measures proposed in the literature all display other disadvantages and weaknesses, such as data limitations and difficulties in expanding data series over a reasonable timespan (Gräbner et al. 2021). In other words, there is no consensus on the best measure to capture trade openness. Therefore, we selected the most commonly used measure in the literature, namely the abovementioned trade flow variable, as a proxy for trade openness, as our study requires a readily available data for a long time span.

  7. The advantages of our chosen methodology are that it is designed to establish the long-run relationship and joint behaviour between these variables, and it takes care of the “endogeneity issue” between the variables analysed. The disadvantage of the technique is that it cannot capture possible non-linear relationships between the variables.

  8. The transformation smooths the data and also helps reduce the pernicious effect of heteroscedasticity (Huh, 2011; Roberts and Nord, 1985).

  9. Granger causality results depend on the number of chosen lags. In common with many papers reporting on temporal causality investigations, we determined the number of lags optimally using the Akaike Information Criterion (see, for example, Auffhammer and Carson, 2008 for discussion).

  10. The reported results follow the Arellano-Bond dynamic panel data estimation procedure, which we believe to be a valid procedure to identify energy consumption in the sample countries. Normal panel methods could pose a problem because of country-specific impacts in a sample comprising many different countries, such as our sample. We employ the twin specification tests developed by Arellano and Bond (1991). The first test is a Hansen J-statistic, which identifies conditions that test the validity of the instruments. The second test checks that the residuals are not serially correlated. It shows that GMM can be deployed either in a one-step or in a two-step process. A two-step process uses residuals derived from the first-step process to build a weighted variance–covariance (VC) matrix when homoscedasticity to the parameters is absent (Arellano and Bond, 1991). In this work, and in line with most papers in the academic literature, a two-step GMM estimator is used.

  11. Institutional quality seems to play a considerable role in the use of natural resources and the sustainability of the environment (Abdala, 2008).

  12. Information and communication technologies (ICT) infrastructure can help to identify advanced, more efficient ways to use energy. ICTs make it possible to find new channels to reduce environmental pollution and promote investment in new and cleaner technologies (Andlib and Khan, 2021).

  13. Financial development (particularly banking and insurance activities) offers effective means to reduce carbon emissions from the environment to improve air quality in various economies (Tan et al. 2021).

  14. Technology plays a crucial role in reducing emanations and to achieve energy conservation targets (Chien et al. 2021).

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Acknowledgements

Revisions in this paper have benefited from comments from three anonymous reviewers of this journal. The authors thank the reviewers for their helpful insights.

Credit author statement

Rudra Prakash Pradhan: conceptualization, writing—reviewing and editing.

Mak Arvin: conceptualization, writing—reviewing and editing.

Mahendhiran Sanggaran Nair: conceptualization, writing—reviewing and editing.

Sara Bennett: conceptualization, writing—reviewing and editing.

John Henry Hall: conceptualization, writing—reviewing and editing.

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Authors and Affiliations

  1. Vinod Gupta School of Management, Indian Institute of Technology, Kharagpur, WB, 721302, India

    Rudra Prakash Pradhan

  2. Department of Economics, Trent University, Peterborough, Ontario, K9L 0G2, Canada

    Mak Arvin

  3. Institute of Global Strategy and Competitiveness, Sunway University Business School, Sunway University, 47500, Bandar Sunway, Selangor, Malaysia

    Mahendhiran Sanggaran Nair

  4. College of Business, University of Lynchburg, Lynchburg, VA, 24501, USA

    Sara Bennett

  5. Department of Financial Management, University of Pretoria, Pretoria, 0028, Republic of South Africa

    John Henry Hall

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Appendices

Appendix 1. Definition of variables

Table 5

Table 5 Notation and definitions
Full size table

Appendix 2. Flow chart of the analysis

To assist the reader, we present a flow chart of the analysis. Given our finding that the chosen variables are cointegrated, we follow the left-hand route towards the end of the flow chart, thus proceeding with a Vector Error-Correction Model (VECM) deployment in place of a Vector Autoregression (VAR) model analysis. Following the VECM analysis to establish causality, several other tests are performed, as discussed in the ‘Robustness and stability of results’ section.

Fig. 2
figure 2

Flow chart of the analysis

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Appendix 3. FMOLS and DOLS estimation results

Table 6

Table 6 Results of FMOLS and DOLS estimations
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Appendix 4. Mixed effects generalised methods of moments (GMM) estimation results

Table 7

Table 7 Results of dynamic GMM estimation
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Appendix 5. Variance decomposition analysis (VDA) results

Figures 3, 4, 5, 6, 7, and 8

Fig. 3
figure 3

Case 1: With CEC, PEG, CO2, OPE, FDI. Variables are defined in Table 5 (Appendix 1)

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Fig. 4
figure 4

Case 2: With OEC, PEG, CO2, OPE, FDI. Variables are defined in Table 5 (Appendix 1)

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Fig. 5
figure 5

Case 3: With GEC, PEG, CO2, OPE, FDI. Variables are defined in Table 5 (Appendix 1)

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Fig. 6
figure 6

Case 4: With EEC, PEG, CO2, OPE, FDI. Variables are defined in Table 5 (Appendix 1)

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Fig. 7
figure 7

Case 5: With BEC, PEG, CO2, OPE, FDI. Variables are defined in Table 5 (Appendix 1)

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Fig. 8
figure 8

Case 6: With NEC, PEG, CO2, OPE, FDI. Variables are defined in Table 5 (Appendix 1)

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Appendix 6. Generalised impulse response functions (GIRFs) results

Figures 9, 10, 11, 12, 13, and 14

Fig. 9
figure 9

Case 1: With CEC, PEG, CO2, OPE, FDI. Variables are defined in Table 5 (Appendix 1)

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Fig. 10
figure 10

Case 2: With OEC, PEG, CO2, OPE, FDI. Variables are defined in Table 5 (Appendix 1)

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Fig. 11
figure 11

Case 3: With GEC, PEG, CO2, OPE, FDI. Variables are defined in Table 5 (Appendix 1)

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Fig. 12
figure 12

Case 4: With EEC, PEG, CO2, OPE, FDI. Variables are defined in Table 5 (Appendix 1)

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Fig. 13
figure 13

Case 5: With BEC, PEG, CO2, OPE, FDI. Variables are defined in Table 5 (Appendix 1)

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Fig. 14
figure 14

Case 6: With NEC, PEG, CO2, OPE, FDI. Variables are defined in Table 5 (Appendix 1)

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Pradhan, R.P., Arvin, M., Nair, M.S. et al. Interface between energy consumption, CO2 emissions, economic growth, and macroeconomic openness in financial action task force countries through the lens of a causality approach. Environ Sci Pollut Res 30, 24256–24283 (2023). https://doi.org/10.1007/s11356-022-23641-1

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