Climate Change: An Emerging Trade Opportunity in South Asia

Abstract

This article investigates emerging opportunities in climate change mitigation in South Asia through trade. Trade can mitigate the climate change issues of a country, region or the world as a whole. Through international agreement (or pressure), trade also creates the opportunity for green jobs that produce environment-friendly goods (EFG), which have less damaging impact on environment. This article examines possible potential trade opportunity of climate-friendly goods (CFG) in South Asia. Applying the gravity model, this article estimates potential trade of CFG in South Asia. It also measures the trade gap as to how well bilateral trade flow performs relative to the mean value of trade as predicted by the model. Here, ‘potential trade gap’ means the gap between actual trade and the predicted trade value. It suggests that there is a scope to improve the export of CFG with trading partners. This article suggests and also highlights an alternative possibility for a climate-friendly export-led growth model in South Asia. It also identifies the potential trade gap of CFG for each regional member and its partners within region and developed countries such as the European Union (EU) and North America (the USA and Canada).

JEL Classifications: Q5, C23, F1

Keywords

Bilateral trade flow climate-friendly goods EFG CFG export India trade opportunity climate change gravity model potential trade gap South Asia SAARC

Introduction

Using the gravity model, this article investigates the potential trade opportunity of climate-friendly goods (CFG) for South Asian countries within region and/or inter-regions especially with the European Union (EU), North America (the USA and Canada) and rest of the world. This study provides evidence on trade opportunity of CFG in South Asia. It also provides certain insights regarding potential trade opportunity of CFG, and this article may assist policymakers to form appropriate policy on climate change¹ and tap the emerging trade opportunity within South Asian Association for Regional Cooperation (SAARC)² or with the rest of the world in the coming years.

Climate change is a new ‘avatar’ emerged in the developing world. Climate change is a shift of climate lasting for an extended period of time. Now, climate change avatar threats to the human civilization and challenge for economic development. Less developed countries (LDCs)³ contribute a little to cause climate change, although they face its harshest impacts. LDCs have the weakest capacity to adapt to these impacts (World Bank, 2008). As per Intergovernmental Panel on Climate Change (IPCC) reports, South Asia is one of the vulnerable regions in the world due to climate change. In this context, does trade play any role in mitigating climate change? Does climate change generate trade opportunity? More specifically, is there any trade opportunity in climate change for South Asian countries? Answering such questions is an urgent need at present situation. It is essential for searching for a trade strategy that mitigates climate change issues in South Asian⁴ region and the world.

Human society adapts to their environment through innovations, adaptation and mitigation.⁵ Basic assumption is that trade plays a major role in disseminating technologies widely. It is expected that advanced know-how and environment-friendly technologies will become more readily available through liberalized trade (Meyer-Ohlendorf and Gerstetter, 2009; World Bank, 2008). Trade liberalization is good for the environment (Antweiler et al., 2001; Copeland and Taylor, 2004; Liddle, 2001). Free trade⁶ has a contradictory impact on the environment, both raising pollution and motivating reduction of it. Trade can help developing countries adapt to generate the export earnings and access updated technologies (World Bank, 2008). Trade is also a potent driver for technological innovations. Trade has a role in the mitigation of climate change through disseminating and exchanging low-carbon technologies. Clean technology improves energy efficiency and reduces environmental impact. Goods, which have a relatively less adverse impact on the environment, are termed, here, as CFG.⁷

Free and liberalized trade can provide or make available such goods for countries which have no access to CFG or wherein domestic industries are unable to produce them in sufficient scale or at affordable prices. For exporters, additional market access can provide incentives to develop new products or technologies which have less green house gas (GHG) emissions and that will reduce certainly global climate impact. Most of the exporters of CFG are from developed nations, but a few exporters from developing countries⁸ and are also becoming important players in the heat and energy management equipment, noise and vibration abatement and in environmental services such as air pollution control and solid waste management.⁹ Few developing countries are among the top 10 importers and exporters in various categories of environmental goods relevant to climate change mitigation (World Bank, 2008). Truly, climate change is a threat and also provides opportunity to redesign the economic activities.¹⁰ In this context, developing countries must focus on production and trade of CFGs and need more emphasis on it. Production structure of the economy tends to shift towards cleaner activities that generate less pollution. Brazil and Mexico are pioneers in the production of clean energy and/or green technologies (World Bank, 2008).

This article analyzes empirically the trade flow between countries applying the gravity model (first conceptualized by Tinbergen, 1962). The gravity model is able to estimate correctly bilateral trade flows making it one of the most stable empirical relationships in economics (Learner and Levinson, 1995). The distribution of CFGs (goods or factors) across space is determined by gravity forces given the size of economic activities at each location. The gravity model is adopted to explain the role of economic size and endowments, distance between trading partner, membership of multilateral agreement, among others on trade of such CFGs or/and sub categories. In particular, this gravity study is a cross-sectional data analysis for estimating the gravity equation considering the bilateral total trade of the CFG exports for the year 2008. The gravity model analysis is useful to explain determinants of exports potential of CFG for South Asian countries (SAARC).

This article is organized as follows: The second section reviews literature. The third section describes data and discusses empirical issues. The fourth section presents and analyzes empirical results. Finally, the fifth section draws concluding observations.

Literature Review

The traditional gravity model is taken from the Newton’s law of gravitation.¹¹ A mass of goods or factors of production supplied at origin i, Y_i, is attracted to a mass of demand for goods or factors of production at destination j, Y_j, but the potential flow is reduced by the distance between them, D_ij. Strictly applying analogy, $X_{ij} \frac{Y_{i} Y_{j}}{D_{ij}^{2}}$ , provides the predicted movement of goods (or factors of production) between i and j, X_ij. The gravity model is initially presented as an intuitive way of understanding trade flows. Typically, the stochastic version of the gravity equation has the form:

X_{ij} Y_{i}^{_{1}} Y_{j}^{_{2}} D_{ij}^{_{3}}_{ij},

where a, b₁, b₂, b₃ are unknown parameters and p_ij is the stochastic term. The gravity model can be written, in a basic econometric form, as

\ln (X_{ij})_{0}_{1} \ln (Y_{i})_{2} \ln (Y_{j})_{3} \ln (_{ij})_{ij},

(1a)

x_ij(1b)_ij

where X_ij indicates exports from country i to country j; Y is each country’s gross domestic products (GDP); x_ij represents trade costs between two countries; distance is the geographical distance between them—as an observable proxy for trade costs; and f_ij is a random error term. Equation (1) is a static econometric model that explains variations in aggregate bilateral trade flows among pair of countries for cross-sections data.

Tinbergen (1962) first introduced the gravity model to explain international trade flows.¹² The gravity model has been used extensively in empirical international trade since its inception. Its popularity in empirical research increased rapidly with the introduction of theoretical gravity by Anderson (1979). Theoretical foundation of gravity model as described by Anderson (1979), Helpman (1987) and Deardorff (1984, 1995) starts with the assumption of frictionless trade. Applying gravity model, McCallum (1995) estimated loss in trade volume and focused on the border puzzle¹³ of large unexplained trade costs when goods are traded across a national border. International border effects are inferred and they matter even with two economies that share a large border and are highly integrated through a regional trade agreement (RTA) such as North American Free Trade Agreement (NAFTA). Trading across borders will cause disconnect in relative prices as insurance, freight, tariffs and non-tariff barriers, and different regulatory structures cause uncertainty and impede trade to some extent.

Frankel et al. (1997) undertake a comprehensive study of regional trading blocs using the gravity model as a major tool. They are able to quantify the amount by which different preferential trade arrangements (PTAs) and regional arrangements such as Asia Pacific Economic Cooperation (APEC) increase trade by adding trade agreement dummy variables into the standard gravity model. Analysis of regional or multilateral trade arrangements using gravity models is now commonplace and important in applied trade theory. Differing econometric specifications of gravity equation are numerous.¹⁴ Baldwin and Taglioni (2006) summarize errors that are frequently repeated in the literature. Popularity of gravity model increased due to Anderson and Wincoop (2003) and now it has become the de facto standard in empirical work.

Trade gravity model is based on the idea that trade volumes between two countries depend on sizes of two countries and distance between them. Distance could be physical, cultural or/and political. Eichengreen and Irwin (1998) and Rauch (1999) demonstrate cultural proxies (border, common language) as dummy variables in empirical gravity equation. The cost of transport is usually a measure of geographical distance. This simple model has been used extensively in analyzing trade and has been successful to a high degree in explaining trade.¹⁵ There is a debate on trade resistances that might limit or promote trade between particular trading partners, often relying on a number of variables to proxy total trade resistances, including trade-related costs. Recently, global climate change itself creates new resistances on international trade after failure of Doha round. This climate resistance also creates the opportunity for trade in new direction in the name of green businesses. Baldwin (1994), Dinda (2011a, 2011b), Nilsson (2000) and Egger (2002) are the most prominent examples in literature that use the term ‘trade potential’ as the expected volume of trade between country pairs that gravity model predicts. It gives a measure of how well a bilateral trade flow performs relative to the mean value as predicted by the model. Following the standard gravity model, this article investigates new direction of potential trade opportunity in CFGs. This study is based on Dinda (2011a, 2011b) and predicts trade opportunity of CFG in South Asia region (SAARC) with reference to Asia.

Data and Empirical Issues

This study has selected 64 CFGs under six digit HS code (2002). Various international organizations (The World Bank, ICTSD, WTO, APEC and the OECD) have recently identified CFGs. The study considers these 64 CFGs as one category¹⁶ and estimates above-mentioned trade indicators for this category. CFG trade data (in value, $1,000) are taken from UN COMTRADE data for the year 2008. Gross domestic production (GDP) and per capita GDP data are taken from World Bank Development Indicators for corresponding years. Distance between countries and other dummy variables are taken from the dist_cepii.xls file of CEPII DATABASE (see Gaulier et al., 2004 and also the website www.cepii.fr). Total observation is reduced after combining all the variables for each pair of trading partners.¹⁷ This filtered data set is used in the empirical analysis.

Contiguity or common border, common language, common ethno group, colony, small country, etc., are used as dummy variables and added in the basic gravity Equation (1). For the estimation and analysis purpose, the following equation is considered here

\begin{matrix} \ln X_{ij}_{0}_{1} \ln {GDP}_{i}_{2} \ln {GDP}_{j}_{3} \ln {PCGDP}_{i}_{4} \\ \ln {PCGDP}_{j}_{5} \ln {DT}_{ij}_{6} D_{contig}_{7} D_{comlang} \\ _{8} D_{comlang ethno}_{9} D_{colony}_{10} D_{comcol}_{11} D_{col 45} \\ _{12} D_{smctry}_{13} {Trf}_{j} {^{}}_{ij}, \end{matrix}

(2)

where X_ij denotes the value of country i exports to country j; GDP_i and PCGDP_i denote the exporting country’s GDP and per capita income (Markusen, 2013), respectively; GDP_j and PCGDP_j denote the GDP and per capita income¹⁸ of the partner of the exporting country, respectively; DT_ij denotes the distance between the exporting country and its partner (importing country); Trf_j is the (weighted average) tariff rate imposed by partner of exporting country; and D_contig, D_comlang, D_{comlang_ethno}, D_colony, D_comcol, D_col₄₅, and D_smctry are the dummy variables for contiguity, common language, colony, common colony, colony from 1945 and small country, respectively. In our regression analysis, we have used the log values of all the variables¹⁹ except dummies. It will reduce heteroscedasticity problem to some extent. The least squares econometric technique is used here and the above gravity Equation (2) is estimated for analysis purpose. Truly, the trade gap is the measured residual which remains unexplained in the model. There are a lot of other unknown factors restricting CFG trade flows. So, trade opportunity may rise with reducing restrictions.

Empirical Results

Preliminary observations are summarized in Tables 1 and 2. Table 1 displays South Asia’s CFG exports share of the world’s CFG exports during 2003–2008. South Asia’s share of CFG export in the world CFG export is very low. South Asia’s CFG export share is less than 1 per cent of the global CFG export. Share of CFG export of the world’s total export increases slowly over time in South Asia (SAARC) region (see Figure 1).

Primary findings are summarized and discussed in Table 2. South Asia’s CFG total trade was nearly $15.174 billion in 2008, out of total CFG trade, actual import and export of CFG were $9.684 and $5.49 billion, respectively (see Figure 2). Out of it, intraregional and interregional trades were $240.1 million and $14.24 billion, respectively. South Asia’s export and import of CFG were $137.8 million and $102.3 million in 2008, respectively. Intraregional demand was nearly 1.58 per cent and only 98.42 per cent for interregional demand of CFG. It is true that internal demand within the region is very low for the CFGs and there is a huge scope to increase it in future to overcome foreseen climate change challenges. Over time, the intraregional demand for CFG will increase with economic development as well as scope of CFG trade.

Table 1.

South Asia’s Trade Share of CFG Exports of the World’s Total CFG Exports During 2003–2008

Year	2003	2004	2005	2006	2007	2008
South Asia’s share of CFG export to the world’s total CFG exports	0.336	0.372	0.515	0.705	0.727	0.945

Table 2.

Region-wise Destination of CFGs Trade of South Asia (SAARC) in 2008

Destination (Region/Country)	Export ($ Million)	Import ($ Million)	Total Trade ($ Million)
SAARC	137.840	102.2833	240.1233
USA	661.6972	462.9056	1,124.603
EU	1,066.706	2,352.720	3,419.426
Rest of the world	3,623.714	6,766.168	10,389.88
Total	5,489.957	9,684.077	15,174.03

Note: Figures are in million USD in 2008.

Figure 1.

South Asia’s Share of CFG Export to the World’s Total CFG Exports

The fitting criteria suggest that the overall fitting of the gravity equation is good in the cross-sectional data analysis (multiple R is around 0.783). The R² value is 0.6135 which means that only 61.35 per cent of the variations in CFGs export of SAARC is explained by the variables used in the equation. Adjusting with lost degree of freedom, adjusted R² is 0.5926 (see Table 3A) that is also a good indicator, here. Other overall variation structure is judged through analysis of variance (ANOVA). Table 3B displays ANOVA table with decomposition of error and F statistic which is large (F statistic 43.142 with [13, 299] degree of freedom). Calculated F is highly significant. This is a moderately high value and also captures the diversity of CFG trade in the sample.

Figure 2.

Regional Destination of CFGs Trade of South Asia (SAARC) in 2008

Table 3.

Results of the Gravity Equation for the Export of CFGs in South Asia in 2008

A: Regression Statistics
Multiple R	0.783246
R square	0.613475
Adjusted R square	0.592566
Standard error	2.364184

B: ANOVA
	Df	SS	MS	F
Regression	13	2,652.48	204.0369	43.142***
Residual	299	1,671.22	5.589364
Total	312	4,323.7

C: Estimated results of the gravity equation for the export of CFGs in South Asia in 2008
Variables	Coefficients	Standard Error	t Statistic
Intercept	−124.223***	8.646	−14.37
GDP_reporter	2.644***	0.141	18.77
GDP_partner	0.942***	0.084	11.27
pcgdp_reporter	6.623***	0.748	8.85
pcgdp_partner	−0.328***	0.11	−2.97
Distw	−0.972***	0.267	−3.64
Contig	0.583	1.051	0.55
comlang_off	−0.116	0.709	−0.16
comlang_ethno	0.555	0.667	0.83
Comcol	0.606	0.392	1.55
col45	1.51	1.415	1.07
Trf	−0.031*	0.016	−1.94
Smctry	−1.475	1.458	−1.01

Note: ‘***’ and ‘*’ denote the statistical level of significant at 1 per cent, 5 per cent and 10 per cent, respectively.

Table 3 presents the estimated results of the gravity equation for CFGs in Asia in 2008. Second column of Table 3C shows the point estimation of coefficients. Statistically significant coefficients are highlighted and marked with stars (see Table 3C). The coefficients of GDP reporter, GDP partner, per capita GDP of reporter, per capita GDP of partner and geographical distance between two countries are statistically significant at 1 per cent level. The coefficient of tariffs is significant at 10 per cent level. Other variables are statistically insignificant in CFG trade. Considering only statistically significant coefficients, the estimated export of CFG is

\begin{matrix} \ln X_{ij} 124 . 22 2 . 64 \ln {GDP}_{i} \\ 0 . 94 \ln {GDP}_{j} 6 . 62 \ln {pcgdp}_{i} \\ 0 . 33 \ln {pcgdp}_{j} 0 . 97 \ln {DT}_{ij} 0 . 03 Trf \end{matrix}

(3)

The export elasticity of CFGs is elastic with respect to GDP of reporting country, which suggests that export of CFG would be increased by more than 2.64 per cent if income of the reporting country increases by 1 per cent. Export elasticity of CFG is inelastic with respect to partner country’s GDP. It suggests that if partner country’s GDP increases by 1 per cent, the export of CFG increases by 0.94 per cent in reporter country’s GDP (i.e., export of CFG from South Asia rises as its partner’s income increases). From this, one can guess that one part of partner country’s internal demand is fulfilled by the production of CFG in reporting countries (i.e., South Asia, here). The export elasticity of CFG is highly elastic (6.62) with respect to per capita GDP (development index) of reporting country. CFG export increases by 6.62 per cent as 1 per cent per capita GDP increases in South Asia (SAARC) region. The export elasticity of CFG is inelastic (−0.33) with respect to per capita GDP development in partner country. CFG export decreases by 0.33 per cent as 1 per cent per capita GDP increases in partner country (or region). It is clear from these findings that export of CFG increases with GDP while declines with per capita GDP in the rest of the world (excluding South Asia or SAARC region).

CFG export of reporting countries directly depends on its economic growth and development, whereas it depends directly on partner countries’ economic growth and inversely on the development of partner countries.²⁰ It is possibly due to increase in internal demand of CFG due to raising awareness of global climate change and related policies and further provides opportunity to produce CFG in Asia. It indicates that opportunity of green business in Asia grows, and business of CFG will expand. The coefficient of distance between country pair is negative as it is expected in the gravity model. Here, the export of CFG elasticity is −0.97 with respect to distance and this observation supports the existing literature²¹ on trade gravity model (Balassa, 1966; Balassa and Bauwens, 1987; Disdier and Head, 2008). There is a negative correlation between geographical distance and trade, that is, an increase in bilateral trade is explained when the transportation cost decreases. Constant term is highly statistical significant, which might capture other unknown variable or factors. Detail depth study is required to explore the reasons behind it.

Using the gravity model, we estimate the predicted export value of the reporting country with its trade partners.²² In this study, the ‘trade potential gap in CFG’ is the trade gap which is defined as the actual trade less than the predicted value. The total estimated potential gap in the export of CFG in South Asia in 2008 is around US$644.149 million. ‘Trade potential gap in CFG’ itself suggests that there is a scope to increase the export of CFGs and technology with its partner. Hence, trade opportunity value of CFG export is US$644.15 million in South Asia in 2008. Following Baldwin (1994), Nilsson (2000) and Egger (2002), some South Asia countries are below the expected trade performance as the literature defines the term ‘potential trade gap’. This trade gap suggests that they could increase the export of CFG. These countries could increase their potential export trade of CFG nearly US$644.15 million in 2008. Among these countries, in terms of unutilized trade opportunity, Pakistan (US$375.56 million) is in the top followed by Sri Lanka (US$211.2 million) and India (US$57.38 million), etc. These major countries have huge untapped potential export of CFG. Intra- and inter-region grouping can be done according to the partner country belonging to South Asia, the EU, America, etc. and accordingly it is identified individual trade partners of the reporting country is identified.

Now, this article discusses about potential trade opportunity of CFG for few selected South Asia countries such as India, Pakistan and Sri Lanka. India has potential to increase its trade opportunity of CFG. Within SAARC region, India can increase CFG export to Pakistan, Bangladesh, Sri Lanka, Nepal and Bhutan; in Asia Pacific region, India has potential trade gap with Armenia, Kazakhstan, Azerbaijan, Japan, Mongolia, Vanuatu, Russia, China, Kyrgyz Republic, New Zealand, Hong Kong, Korean Republic, Indonesia, Iran, Philippines, etc. The most important and encouraging India’s CFG trade partners in Europe are Luxembourg, the UK, Latvia, Cyprus, Greece, Hungary, Slovenia, Slovakia, Austria, Finland, Ireland, Poland, Spain, Lithuania, Bulgaria, Romania, Denmark, Sweden, France, Italy and Czech Republic. India has trade potential to increase trade of CFG with Canada. India is leading in CFG trade in the SAARC region, but it is still lagging behind compared to other emerging countries in the world. India has potential to raise CFG production with developing technologies and thereby India will increase its export of CFG to Asia, the EU and the USA.

Pakistan has huge trade potential in SAARC, Asia Pacific and the EU. Within SAARC, potential trade partners of Pakistan are India, Bangladesh, Nepal and Afghanistan. Pakistan can increase CFG export to these countries. Other potential trade partners of Pakistan are New Zealand, Australia, Jordan, Bahrain, Oman, Sudan, Vietnam, Azerbaijan, Kazakhstan, Kyrgyzstan, Uzbekistan, Congo, Egypt, South Africa, Nigeria, Tanzania, Austria, Ireland, Italy, Greece, Canada, etc. Within SAARC region, Sri Lanka fully utilizes export potential of CFG except with India. Sri Lanka has potential trade gap with India. Other potential trade partners are Peru, South Africa, Oman, Uganda, Angola, Saudi Arabia, Philippines, New Zealand, Vietnam, Romania, Jordan, Norway, UAE, Denmark, Sweden, Kuwait, Kyrgyzstan, Uruguay, Indonesia, Latvia, Ukraine, Australia, Japan, etc. So, individual and regional partners are identified in this study. It will help to form adaptive policy and settle the bilateral agreement with trading partners.

Conclusion

Applying the gravity model, this article measures the potential export trade gap of climate friend goods in South Asia. Through trade gap, this article estimates the value of trade opportunity of CFG in South Asia (or SAARC) and identifies potential trading partners. This article suggests increasing the export of CFG among trading partners. The total estimated potential export of CFG in South Asia is nearly US$15.5 billion in 2008. This study contributes to the empirical measurement of potential trade opportunity of CFG for individual country and also quantifies it for each trading partner. It will assist the policymakers and/or the governments to formulate economic policy regarding CFGs and negotiate trade partners in right direction to tap the potential opportunity of CFG. It may stimulate CFG export-led growth in South Asia and also mitigate climate change issues.

There is a huge variation in the potential trade gap in CFG among nations in South Asia. One of the major reasons is the variation of trade restriction in South Asian countries. Other reasons may be socio-political conditions and economic development policies which vary widely among South Asian countries. The reasons for untapped potential export gap in CFGs might be the lack of awareness, unavailability of technology, lack of skilled labour for production of CFG, unfavourable business environment, weak governance, inappropriate government policy towards CFGs, lack of trade facilitations, etc. More depth firm-level study is needed to explore these in details.

Footnotes

Acknowledgements

I am grateful to an anonymous reviewer of this journal for valuable comments and suggestions. I am also grateful to Mia, UNESCAP, Bangkok, for her suggestion regarding application of gravity model for estimation and conceptualization on CFGs and green technology trade. I also thank to the participants in the 4th ICRN Conference at IIT Madras, during 26–27 October 2013. The usual disclaimer applies.

Notes

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