Carbon neutrality targets, optimal environmental management strategies & the role of financial development: New evidence incorporating non-linear effects and different income levels
Introduction
At the G7 Summit in Carbis Bay, England, in June 2021, it was agreed to take genuine action on tackling climate change and a pledge was made to raise $100 bn a year to help poor countries cut carbon dioxide (CO2) emissions. The pledge is a recognition that there is a huge gap between developed countries and developing nations in relation to energy use efficiency and environmental quality, caused by the economic development level.
Many researchers have studied the relationship between economic growth and CO2 emissions, to find the factors influencing CO2 emissions and explore remedies for improving environmental quality. These factors include energy consumption, population growth, GDP, trade openness, FDI, urbanisation, productivity, and new technology adoption. In recent years, more and more research has paid attention to the role of financial development in the increase or decrease of CO2 emissions. Researchers have argued that financial development, consisting of financial institutions and financial markets, is a crucial determinant affecting CO2 emissions.
However, results are mixed (Abbasi and Riaz, 2016; Haseeb et al., 2018; Shoaib et al., 2020). Several studies report that financial development leads to a decrease in CO2 emissions (Shahbaz et al., 2013a, 2013b, 2013c, 2018, 2013b; Charfeddine and Kahia, 2019), while others suggest that financial development tends to result in environmental degradation (Xu et al., 2018; Nasir et al., 2019; Shahbaz et al., 2020). Thus, there are alternative accounts of the relationship between financial development and CO2 emissions.
On the one hand, some studies suggest that financial development increases CO2 emissions, because a well-developed financial sector mitigates information asymmetry and funds more production, which stimulates energy supplies and consumption (Sadorsky, 2010; Zhang, 2011; Dogan and Turkekul, 2016). Overall, financial development facilitates greater economic activity, which is associated with increased emissions.
On the other hand, financial institutions (e.g., banks) can provide funds to support energy-efficient companies, projects and technologies (Tamazian et al., 2009; Islam et al., 2013; Jiang and Ma, 2019); and if there is a well-functioning stock market, energy-efficient firms can quickly raise money to invest more in green energy technologies (Jiang and Ma, 2019). Furthermore, with greater financial development, governments can set appropriate financial and monetary regulations and policies as part of the institutional framework (Nguyen et al., 2021), to influence financial institutions and stock markets in order to achieve a nation's ultimate CO2 emission targets.
Various other explanations might exist for the differing empirical results. The findings could be affected by many specific factors such as the countries and regions studied, data coverage, and associated variables. A further explanation might lie in the modelling approaches taken by previous studies. Most have assumed an underlying linear relationship between variables. However, as Jiang and Ma (2019) hint, that the nonlinear features of the relationship between financial development and CO2 emissions might reflect in institutions, governmental policies, or income level of a country. As such, linear analytic approaches should have a bias in estimation, and the results could be misleading because they might miss heterogeneous impacts of financial development on CO2 emissions. The evidence so far is suggestive, but not conclusive.
In response, this paper uses econometric techniques – including ARDL (autoregressive distributed lag) and NARDL (nonlinear ARDL) models, and a panel causality test – to examine the impact of financial development on CO2 emissions. The data sample covers 61 countries, comprising 36 high-income countries and 25 middle-income countries, during the period 1990–2018. The models also incorporate other variables; energy use, FDI and GDP. We pay special attention to the difference in results between linear ARDL and nonlinear ARDL (NARDL) models. In particular, our fresh evidence using NARDL shows that the relationship between financial development and CO2 emissions is asymmetric, and the positive shocks of financial development have a more profound effect. The results also reveal that the roles of financial development in CO2 emissions are distinctive between high- and middle-income economies. In the long run, financial development helps to minimise CO2 emissions for high income economies, but it raises CO2 emissions and thereby decreases environmental quality for middle-income economies.
The remainder of the paper is structured as follows. Section 2 reviews relevant literature about the linkages between financial development, CO2 emissions and economic growth. Section 3 provides theoretical reasoning for the selection of broad money and stock market capitalisation as the proxies for financial development. Section 4 discusses data and methodology. Section 5 presents the empirical results. In this section, we present both linear and nonlinear ARDL models. Section 6 highlights contributions, limitations and policy implications.
Section snippets
Linkages between financial development, CO2 emissions and economic growth
Countries’ CO2 emissions tend to vary with their level of economic development (Blackburn et al., 2012; Berdiev and Saunoris, 2016; Canh et al., 2020). In the literature, considerable attention has been paid to the association between financial development (represented by the financial sector) and CO2 emissions or other, broader effects such as environmental pollution, environmental quality, environmental degradation, etc. Financial development can provide effective financing at a lower cost to
Theoretical foundation
According to Friedman and Schwartz (1963), the Quantity Theory of Money (QTM) is described by Irwing Fischer's ‘equation of exchange’ (Fisher, 1911). The theory may be useful in offering a deeper understanding of how financial development affects an economy. In the formula M.VT = Σi (pi. qi) = pTq, M represents the total sum of money circulating in an economy at a given point in time; VT denotes the money's transaction velocity, which shows how rapidly it changes hands in an economy (M); p
Data and descriptive statistics
Based on data availability, we employ a panel dataset of 61 countries categorising as high, upper-middle, and lower-middle incomes for the 1990–2018 period, utilising The World Bank Group, 2021a, The World Bank Group, 2021b data from the World Development Indicators. The detail of selected countries for all three groups classified by The World Bank Group (2021b) is reported in Table 1. However, to achieve optimal panel analysis, we subsequently merge the upper-middle and lower-middle income
Empirical results
This section presents the results of the tests described in the previous section.
Discussion and conclusion
Unlike most similar studies (see some examples in Section 2), which examine the impact of financial development on CO2 emissions using a linear framework, the current paper is one of just a few pioneer studies that adopt both linear and nonlinear approaches. This means that the bias from linear analysis can be minimised, and richer explanations of the relationship can be provided. Herewith, we summarise our findings and discuss contributions and policy implications.
First, the paper argues that
Credit author statement
All the authors have made significant contribution to the paper. The details of tasks are as follows: Natthinee Thampanya: Conceptualisation, Data collection, Methodology, Software, Formal analysis, Writing – original draftpreparation. Junjie Wu: Informal Formal analysis, Writing – review & editing. Christopher Cowton: Writing – review & editing.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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