Globalisation and its effect on pollutio

Environ Sci Pollut Res DOI 10.1007/s11356-017-9950-0 RESEARCH ARTICLE Globalisation and its effect on pollution in Malaysia: the role of Trans-Pacific Partnership (TPP) agreement Sakiru Adebola Solarin & Usama Al-mulali & Pritish Kumar Sahu Received: 28 November 2016 / Accepted: 11 August 2017 # Springer-Verlag GmbH Germany 2017 Abstract The main objective of this study is to investigate the influence of the globalisation (Trans-Pacific Partnership (TPP) agreement in particular) on air pollution in Malaysia To achieve this goal, the Autoregressive Distributed Lag (ARDL) model, Johansen cointegration test and fully modified ordinary least square (FMOLS) methods are utilised CO2 emission is used as an indicator of pollution while GDP per capita and urbanisation serve as its other determinants In addition, this study uses Malaysia’s total trade with 10 TPP members as an indicator of globalisation and analyse its effect on CO2 emission in Malaysia The outcome of this research shows that the variables are cointegrated Additionally, GDP per capita, urbanisation and trade between Malaysia and its 10 TPP partners have a positive impact on CO2 emissions in general Based on the outcome of this research, important policy implications are provided for the investigated country Keywords CO2 emissions GDP per capita Urbanisation Trans-Pacific Partnership agreement Responsible editor: Philippe Garrigues * Sakiru Adebola Solarin sasolarin@mmu.edu.my Usama Al-mulali usama.almulali@mmu.edu.my Pritish Kumar Sahu pritish.sahu@mmu.edu.my Faculty of Business, Multimedia University, 75450 Melaka, Malaysia Introduction The rising inter-linkages among various economies across the world have multifaceted impacts on the socio-economicpolitical aspects of life Associated with these inter-linkages is the rising volume of international trade, which has both positive and negative implications on the environmental quality of the trading nations International trade can aid the positive spread of environmentally friendly practices and technologies from advanced to developing nations It can also decrease pollution in emerging economies through, for instance, the importation of cleaner technologies or through the development of better environmental regulations and standards Nations can utilise trade relations to encourage their emitting neighbours to decrease pollution or join transnational environmental conventions (Naughton 2010) Since environmental quality is considered a normal good, the demand for it will increase when trade raises income (Onder 2012) On the other hand, fossil fuels including coal, natural gas and oil are required in the process of producing goods and services for international transactions The direct effects of industrial production, transportation and deforestation, which are associated with international trade, include emissions and environmental degradation (Huwart and Verdier 2013) It is vital for studies that examine the effects of trade on the environment not to overlook the pollution haven hypothesis (PHH), which envisages that the removal of trade barriers makes dirty companies to migrate to countries with loose environmental standards Developing nations that are poor usually function as pollution havens and thereby generate more emissions These poor nations trade more due to the availability of a neighbouring large market (Naughton 2010) There are several trade agreements available in this era, and the main intention of these agreements is to increase international trade The growing realisation of the role of free trade Environ Sci Pollut Res agreements in improving the overall economic performance of the participant countries resulted in intensifying the trade negotiations across the globe since the late 80s Developing and emerging economies are particularly interested in such agreements as they expect to strengthen their market access, economic growth, income level and living standards, to mention a few The Trans-Pacific Partnership (TPP) agreement is an agreement signed on the fourth of February 2016 after 19 rounds of tough negotiations that took over years to achieve Regarded as the biggest trade agreement of the twenty-first century, the TPP agreement is the successor of the Trans-Pacific Strategic Economic Partnership Agreement, or TPSEP, which was signed by Brunei, Chile, New Zealand and Singapore in 2005 The TPP trade bloc comprises of 11 nations, including Australia, Brunei, Canada, Chile, Japan, Malaysia, Mexico, New Zealand, Peru, Singapore and Vietnam.1 These countries account for over 14% of the global GDP and almost 7% of the global population in 2015 (United Nations 2017) It is one of the several Mega-Regional Trade Agreements (MRTAs) that have emerged since the mid-1990s As a deep and comprehensive trade agreement, the TPP covers traditional barriers to trade in goods and services (e.g tariffs, restrictions on the movement of professionals), investment activities and other trade-related areas Such areas include formal restrictions on some trade and investment activities, burdensome and inconsistent regulations, varying treatment of intellectual properties, differing labour and environmental standards, issues specific to small and medium-size enterprises and new challenges arising from rapidly growing digital technologies (World Bank 2014) This monumental trade deal raises some widespread speculations as to how this would affect countries in the Asian region, particularly the relatively smaller countries Undoubtedly, it is evident that the free trade plays a significant role to the economic growth and welfare of the countries that are part of the agreements (Dollar 1992; Dollar and Kraay 2004; Chang et al 2009; Hur and Park 2012) In contrary, there are studies which established a negative or insignificant impact of free trade on economic growth and welfare (Rodrik et al 2004; and Wacziarg and Welch 2008 etc.) However, in the context of TPP, there are limited empirical studies, mainly because of the confidentiality of the TPP (Cororaton and Orden 2015; Deardorff 2013; Petri and Plummer 2012; Cheong 2013) These studies have primarily estimated the gains, losses and the economic welfare for several The USA was initially a signatory to the agreement USA has formally withdrawn from the agreement through presidential memorandum Therefore, it is virtually impossible that USA may ratify the agreement participating and important non-participating countries in the region Mostly, these studies have estimated that Malaysia is likely to gain in economic fronts during the post-tariff elimination period The agreement is also expected to generate a rise in the direct cost of medicine and the cost of treating diseases (Lee et al 2016) However, we are not aware of any empirical studies exclusively focusing on the effect of post-TPP tariff elimination on emissions The objective of this paper is to examine the potential role of TPP on CO2 emissions in Malaysia for the period of 1970–2014 We contribute to the existing literature on the determinants of CO2 emissions in two important ways Firstly, we use indices for TPP as additional determinants of CO2 emissions The previous papers have merely used proxies for international trade without considering the potential role of TPP on emissions Secondly, we incorporate structural breaks in the estimation process, including the unit root testing and cointegration procedures Specifically, we introduce a residual augmented least squares (RALS) unit root test on the series involved in a trade-emission exercise The method is a powerful unit root testing method that provides for endogenously determined structural breaks Unlike most of the existing linearity tests, the method is still robust in the presence of nonlinearity The RALS procedure provides for any evidence of non-normality, including asymmetry and fattailed distributions (Meng et al 2014) We focus on Malaysia for two reasons Firstly, among the 11 TPP members, it is a typical example of developing countries that are facing rising levels of emissions in spite of the various proactive actions of the governments to curb the menace Although Malaysia’s share in the global emissions is very low, the intensity levels of the country’s emissions are higher than the global average in the energy sector (Economic Planning Unit 2015) According to the BP Statistical Review of World Energy (2016), CO emissions in Malaysia increased from 9.8 million tonnes in 1970 to 60 million tonnes in 1990 and further increased to 258 million tonnes in 2014 Among the efforts of the government to reduce pollution was the introduction of the Environmental Quality Act of 1974 aimed at ensuring that the environment is clean, healthy and safe In order to reinforce environmental regulations, the Act has been developed over the years In the transportation sector, initiatives were introduced to curb motor vehicles’ emissions in addition to encouraging a greater use of biofuels and energy efficient vehicles (Economic Planning Unit 2015) Secondly, there is a huge divergence in opinion on the implications of the agreement on the Asian economies, including Malaysia (Lee et al 2016) Therefore, the current study is an attempt to Environ Sci Pollut Res present an important implication of the agreement on Malaysia Literature review It is well known that CO2 emissions are one of the main contributors to greenhouse gas emissions, which present a major dilemma for the globe due to the increase in human activities worldwide Therefore, a large number of empirical studies examined the main factors that contributed to the environmental pollution Numerous scholars had already made a detailed summary on the empirical studies that examined the environmental pollution model during the period of 2004–2014 (Zhang 2011; Wang et al 2011; Saboori and Sulaiman 2013; Kivyiro and Arminen 2014; Al-mulali et al 2015a, b; Baek 2015; and so forth) Therefore, this research will provide a summary of the recent 2015–2016 literature (presented in Table 1) that investigated the main determinants of environmental pollution Despite the different methods and countries the previous studies had investigated, it is clear that real GDP (Shahbaz et al 2015, 2016a, b, c, 2017a; AlMulali and Ozturk 2015, 2016; Al-mulali et al 2015a, b; Özbuğday and Erbas 2015; Seker et al 2015; Zakarya et al 2015; Haq et al 2016; Ertugrul et al 2016; Zhu et al 2016; Dogan and Seker 2016; Dogan and Turkekul 2016; Charfeddine and Khediri 2016; Wang et al 2016; Bento and Moutinho 2016), energy consumption especially from nonrenewable sources (Shahbaz et al 2015, 2016a, c, 2017b; Al-Mulali and Ozturk 2015, 2016; Almulali et al 2015a, b; Seker et al 2015; Zakarya et al 2015; Haq et al 2016; Ertugrul et al 2016; Rafiq et al 2016; Zhu et al 2016; Dogan and Seker 2016; Charfeddine and Khediri 2016; Wang et al 2016; Bento and Moutinho 2016) and population (from total and urban population) (Al-Mulali and Ozturk 2015, 2016; Özbuğday and Erbas 2015; Zhu et al 2016; Al-mulali et al 2016b; Wang et al 2016; Shahbaz et al 2016c; Ahmed et al 2017) are the main contributors to environmental degradation Regarding the other determinants, namely trade openness and financial development, the results are not uniform A number of studies found that trade openness (Al-mulali et al 2016a, b; Al-mulali and Ozturk 2015; Ertugrul et al 2016; Dogan and Seker 2016; Shahbaz et al 2016c) and financial development (Shahbaz et al 2015; Al-mulali et al 2016a; Charfeddine and Khediri 2016; Bento and Moutinho 2016) increase pollution However, other scholars found that both variables mitigate pollution (Al-mulali et al 2016a; Haq et al 2016; Rafiq et al 2016; Zhu et al 2016; Charfeddine and Khediri 2016; Al-mulali and Ozturk 2016) However, most of the scholars that examined the effect of renewable energy consumption (clean sources of energy) on pollution reached the same conclusion which indicated its significant effect on mitigating pollution levels (Al-mulali et al 2016a; Dogan and Seker 2016; Al-mulali and Ozturk 2016; Bento and Moutinho 2016) Despite the recent considerable number of studies, the influence of globalisation on environmental degradation is rarely analysed (Shahbaz et al 2015, 2016a) However, the results of these studies are similar to the older research that investigated this relationship (Christmann and Taylor 2001 and so forth) because they used a general definition of globalisation (export plus imports divided by total GDP) However, a more specific definition can provide a better picture to understanding the relationship between globalisation and pollution Methodology Model and data The STRIPAT (which represents Stochastic Impacts by Regression on Population, Affluence and Technology) framework is adopted to examine the factors of environmental degradation or pollution in Malaysia According to the STRIPAT, the magnitude of the environmental quality is shaped by affluence level or economic prosperity, demography and the level of technology in a country (Dietz and Rosa 1994, 1997; York et al 2003) The size of affluence or economic prosperity is reflected in the average propensity to consume (APC) As the average consumption in the economy rises, pollution level also rises A usual indicator of consumption in the economy is GDP per capita Although GDP per capita shows the value of an economy’s production, it is usually expected that consumption rises when production rises (York et al 2003) Urbanisation captures the dynamism of a population and can represent the demographic changes of a country The process of urbanisation and the growth of cities are the result of increase in population and population density Technology signifies the other determinants of environmental quality beyond affluence and demography (York et al 2003) Hence, we adopt globalisation as a proxy of technology The transfusions of technological innovations are associated with the rapid rate of globalisation Through technical and scientific seminars, media, internet and several other communication mechanisms, globalisation promotes knowledge transmission at a much greater pace compared to past experiences (Archibugi and Pietrobelli 2003) Transfer of technology from the developed countries to the developing ones is a common phenomenon of the contemporary era On the other hand, trade influences energy demand and environmental Time period 1970–2012 1980–2010 1971–2011 1971–2012 1971–2011 Shahbaz et al (2015) Al-Mulali et al (2016a) Haq et al (2016) Shahbaz et al (2016a) Ertugrul et al (2016) Literature review Author(s) Table 19 African countries Morocco Seven regions India Country/region The variables are cointegrated Globalisation, financial development, energy consumption and GDP increase CO2 emission while GDP square reduces it The variables are cointegrated GDP have a positive effect on all regions expect for sub-Saharan Africa GDP square reduces CO2 emission in all regions expects for sub-Saharan Africa Renewable energy significantly reduces CO2 emission in Western Europe, East Asia and the Pacific, South Asia and The Americas while it has positive effect on CO2 emission in the Middle East Trade openness increases CO2 emission in Central and Eastern Europe, South Asia and sub-Saharan Africa while trade openness reduces CO2 emission in Western Europe Urbanisation increases CO2 emission in Central and Eastern Europe, East Asia and the Pacific, Middle East and North Africa, South Asia and The Americas Financial development increases CO2 emission in Central and Eastern Europe, East Asia and the Pacific, South Asia and sub-Saharan Africa The variables are cointegrated GDP square and energy consumption increases CO2 emission while GDP and trade openness reduces it The variables are cointegrated in Algeria, Angola, Cameroon, Congo Republic, Ghana, Kenya, Libya, Morocco, Nigeria, South Africa, Sudan, Tanzania, Togo, Tunisia, Zambia and Zimbabwe Energy intensity increases CO2 emission Algeria, Angola, Cameroon, Congo Republic, Ghana, Kenya, Libya, Morocco, Nigeria, South Africa, Sudan, Togo and Tunisia while energy intensity reduces CO2 emission in Zambia and Zimbabwe Globalisation decreases CO2 emission Angola, Cameroon, Congo Republic, Egypt, Kenya, Libya, Tunisia and Zambia but increases CO2 emissions in Ghana, Morocco, South Africa, Sudan and Tanzania GDP square reduces CO2 emission in Algeria, Cameroon, Congo Republic, Morocco, Tunisia and Zambia but its positive in Sudan and Tanzania The Bayer–Hanck cointegration approach, ARDL and VECM Granger causality Pedroni and Fisher type cointegration, panel dynamic OLS and VECM Granger causality Johansen cointegration and VECM Granger causality CO2 emission, energy consumption, GDP, GDP square, financial development and globalisation CO2 emission, GDP, GDP square, renewable energy consumption, trade openness and financial development CO2 emission, GDP, GDP square, energy consumption and trade openness CO2 emission, GDP, GDP square, energy intensity and globalisation ARDL and VECM Granger causality ARDL approach Main results Method Variables Environ Sci Pollut Res Time period 1996–2012 1980–2010 1981–2011 1981–2011 1980–2009 1971–2009 1980–2012 1975–2011 1974–2010 1990–2012 1990–2012 Author(s) Al-mulali and Ozturk (2015) Rafiq et al (2016) Zhu et al (2016) Al-mulali et al (2015a, b) Al-mulali et al (2016b) Özbuğday and Erbas (2015) Dogan and Seker (2016) Charfeddine and Khediri (2016) Seker et al (2015) Wang et al (2016) Al-mulali and Ozturk (2016) Table (continued) 27 advanced countries China Turkey United Arab Emirates (UAE) European Union 36 countries 58 different countries Vietnam ASEAN-5 Emerging countries CO2 emission, population, GDP, energy efficiency, urbanisation, car ownership and percentage of nonagricultural industry CO2 emission, GDP, GDP square, renewable and nonrenewable energy, Ecological footprint, GDP, trade openness, urbanisation, renewable energy CO2 emission, GDP, population, manufacturing output, energy efficiency and renewable energy CO2 emission, GDP, GDP square, trade openness, renewable energy consumption and nonrenewable energy consumption CO2 emission, GDP, GDP square, financial development, financial development square, urbanisation, trade openness and energy consumption CO2 emission, GDP, GDP square, FDI and energy consumption CO2 emission, GDP, population, energy consumption, trade openness, foreign direct investment and industrial output CO2 emission, GDP, capital, labour, renewable and nonrenewable energy consumption, exports and imports Ecological footprint, energy consumption, urbanisation, trade openness, industrial output, political stability Population, affluence, renewable energy, nonrenewable energy, urbanisation and trade openness CO2 emission, GDP, GDP square, energy consumption and trade openness China, India, South Korea, Brazil, Mexico, Indonesia, South Africa, Turkey, Thailand and Malaysia Middle East and North African countries Variables Country/region Kao and Fisher type cointegration, FMOLS and VECM Granger causality Panel OLS ARDL Gregory and Hansen and Hatemi-J cointegration Dynamic ordinary least square (DOLS) Common correlated effects estimator model Generalised method of moments (GMM) ARDL Fisher type cointegration, OLS and fully modified OLS and panel quantile regression Three 2nd-generation heterogeneous linear panel models Pedroni cointegration, FMOLS and VECM Granger causality Method The variables are cointegrated GDP, FDI and energy consumption increase CO2 emission while GDP square reduces it Population, GDP, car ownership, percentage of nonagricultural industry and urbanisation increase CO2 emission while energy efficiency reduces it The variables are cointegrated GDP, nonrenewable energy and urbanisation increase CO2 emission while trade openness, Population, energy intensity and nonrenewable energy consumption increases CO2 emission while urbanisation and renewable energy has no effect on CO2 emission However, trade openness reduces CO2 emission The variables are cointegrated Energy consumption, GDP and population increases CO2 emission while trade openness and FDI reduces it GDP, fossil fuel energy consumption, capital and imports increase CO2 emission while labour reduces it Export and renewable energy have no significant effect on CO2 emission GDP, trade openness, urbanisation and renewable energy increases ecological footprint GDP, energy efficiency, population, manufacturing output increases CO2 emission while renewable energy reduces it GDP and nonrenewable energy consumption increases CO2 emission while renewable energy consumption, trade openness and GDP square reduce it GDP and financial development increase CO2 emission while GDP square, financial development square, urbanisation and trade openness reduce it The variables are cointegrated in Thailand, Turkey, India, Brazil, China, Indonesia and Korea GDP, energy consumption and trade openness increase CO2 emission in the long run GDP square reduces CO2 emission in Turkey, India, China and Korea The variables are cointegrated All the variables expect for political stability increase CO2 emission in the long run Main results Environ Sci Pollut Res 105 countries Brazil, Russia, India, China and South Africa Australia 1980–2014 Shahbaz et al (2017a) 1970–2012 1990–2012 Zakarya et al (2015) Malaysia Shahbaz et al (2017b) 1970–2011 Shahbaz et al (2016c) Italy Association of Southeast Asian Nations (ASEAN) members 1960–2011 Bento and Moutinho (2016) Country/region Ahmed et al (2017) Time period Author(s) Table (continued) CO2 emission, GDP, GDP square, renewable energy consumption nonrenewable energy consumption, transport energy consumption and trade openness CO2 emission, GDP, population, energy consumption and globalisation CO2 emission, GDP and trade openness CO2 emission, GDP, FDI and energy consumption trade openness, urbanisation and energy prices CO2 emission, GDP, GDP square, nonrenewable electricity consumption, renewable electricity consumption, merchandise exports and imports CO2 emission, GDP, urbanisation, urbanisation square, energy consumption and trade openness Variables ARDL and VECM Granger causality Pedroni cointegration, DOLS and FMOLS and VECM Granger causality Pedroni and Westerlund cointegrations, DOLS and FMOLS and VECM Granger causality Pedroni cointegration, FMOLS and VECM Granger causality ARDL and VECM Granger causality ARDL Method The variables are cointegrated GDP, nonrenewable energy consumption and trade openness increase CO2 emission, while transportation, energy consumption and GDP square decrease it The variables are cointegrated Population, energy consumption, CO2 emission and GDP decrease it The variables are cointegrated GDP and trade openness increase CO2 emission GDP square, renewable energy and energy prices reduce it The variables are cointegrated GDP, nonrenewable electricity consumption and trade openness indicators increase CO2 emission while renewable electricity consumption and GDP square reduce it The variables are cointegrated GDP, energy consumption, urbanisation square and trade openness increase CO2 emission while GDP square increases it All the variables increase CO2 emission Main results Environ Sci Pollut Res Environ Sci Pollut Res quality by transferring the pollution inclined technology to nations where environmental regulations are feeble, especially in the underdeveloped economies Therefore, the equation is specified as follows: lnEMI t ẳ lnRGDPt ỵ lnURBt ỵ lnTPPt ỵ lnDUM t ỵ t ð1Þ where EMIt is CO2 emissions (metric tonnes per capita), RGDPt is GDP per capita (constant 2010 US$), URBt is the urban population (% of total population) and TPP t is Malaysia’s total trade with the other 10 TPP countries (which is Malaysia’s total exports into the other 10 TPP countries plus Malaysia’s total imports from the other 10 TPP countries) as a share of Malaysia’s GDP TPPt is used as an indicator for globalisation in this paper In the subsequent equations, we further use many proxies for globalisation including Malaysia’s total trade with each TPP country as a share of Malaysia’s GDP, Malaysia’s total trade with all trading countries as a share of Malaysia’s GDP total trade and Malaysia’s total trade with TPP countries plus the USA as a share of Malaysia’s GDP DUMt is the dummy variable, which is included in the equation to capture the structural breaks Although we provide for a maximum break of two periods, the unit root tests show that there is one significant structural break in the case of CO2 emission Therefore, we will use a single break in the bound test A similar paper that has used one break is Solarin et al (2017) Economic activities are mainly responsible for CO2 emissions in several cases as they involve fuel combustion in the residential, industrial, power generation and transportation sectors, which increase greenhouse emissions (GHGs) Deforestation generates emissions and decreases CO absorption by plants Natural processes, such as plant matter decay, also cause pollutions Countries with fossil fuels dominating their energy mix are likely to be experiencing increases in emissions With energy inefficiencies and prevalence of energy wastage, it is expected that economic activities will lead to more emissions in the country Previous studies that have real GDP in their pollution equations include Shahbaz et al (2013) and Solarin and Lean (2016) Trade is, in some ways, a type of technology Moreover, it is connected with human activities that cause emissions such as transportation, industrial production and deforestation (Huwart and Verdier 2013) The liberalisation of trade in many developing countries has lured multinational corporations to move their plants from high-income nations to low-income countries and these companies pay poor salaries and not usually fulfil the environmental laws imposed by the highincome nations (Hubbard and O’Brien 2013) Trade openness has been criticised on the basis that it encourages more production which negatively affects the environmental quality because of the poor production techniques (Ling et al 2015) Furthermore, multinational firms tend to move their production bases to the developing countries because of slack environmental regulations (Ling et al 2015) Therefore, it is expected that the increase in trade will lead to more emissions in the country Previous works that have added trade indicators in the pollution equations include Shahbaz et al (2016d) Several emerging economies are undergoing economic transformation that will eventually cause physical expansion of the urban centres The urban areas are usually energyintensive with high propensity of economic activities, which are frequently driven by fossil fuels that cause environmental degradation The quick pace of urbanisation in recent decades will probably cause the snowballing of energy demand and pollution Therefore, it is expected that increase trade will lead to more emissions in the country Previous works that have added urbanisation in the pollution equations include Solarin and Lean (2016) The CO2 emission dataset is collected from BP Statistical Review of World Energy, while the data for GDP, exports and imports are collected from World Integrated Trade Solution (Wits) supplied by World Bank database for the period of 1970–2014 Vietnam is the only exception as the data for exports and imports were available for only the period of 1975–2014 The urban population ratio is collected from the world development indicators of the World Bank The data for population (which is used as a divisor in order to obtain emission per capita, real GDP per capita and urban population ratio) is generated from world development indicators of the World Bank The descriptive analysis is presented in Table Worldt denotes Malaysia’s total trade with all trading countries as a share of Malaysia’s GDP, and UTPP t represents Malaysia’s total trade with all TPP countries plus the USA as a share of Malaysia’s GDP The other variables have been defined earlier The variables are reported in their original forms The mean statistics show that the trade between Malaysia and the TPP countries was almost 38% of the total trade of Malaysia in the period of 1970–2014 The Jarque- Table Descriptive analysis Series Mean Standard deviation Jarque-Bera EMIt 4.288 5500.150 53.377 47.270 125.958 66.780 2.520 2523.443 12.616 11.657 38.626 20.339 3.757 (0.153) 3.291 (0.193) 3.291 (0.193) 2.942 (0.230) 3.729 (0.155) 3.609 (0.165) RGDPt URBt TPPt WORLDt UTPPt The parenthesis contains the probability values Environ Sci Pollut Res Table Two-break LM and RALS-LM unit root tests Country LM stat RALS-LM stat τ × LM τ × RALS ‐ LM ρ2 TB Break (1) RALS-LM critical values Break (2) 1% 5% 10% lnEMIt Δ ln EMIt lnRGDPt Δ ln RGDPt lnURBt Δ ln URBt lnTPPt Δ ln TPPt lnAustraliat Δ ln Australiat lnBruneit Δ ln Bruneit lnCanadat Δ ln Canadat −1.798 [0] −5.675a [0] −2.291 [3] −5.008a [2] −1.244 [2] −5.960a [1] −3.436 [0] −5.360a [2] −3.666 [0] −5.844a [0] −3.683 [0] −8.844a [3] −3.366 [0] −8.941a [0] −2.901 [0] −6.054a [0] −2.365 [3] −5.966a [2] 1.043 [2] −5.320a [1] −3.341a [0] −5.055a [2] −3.582 [0] −5.677a [0] −2.043 [0] −8.699a [3] −3.215 [0] −10.331a [0] 0.858 0.846 0.682 0.640 0.288 0.197 0.928 0.652 0.848 0.874 0.862 0.899 0.778 0.687 1994 1997 1984 1996 1990 1976 1976 1985 1996 1982 1975 1983 1977 1990 – – 1996 1999 2000 1981 1999 1993 1999 1985 1979 1994 2005 1993 −4.116 −4.109 −4.395 −4.350 −3.795 −3.592 −4.639 −4.363 −4.567 −4.594 −4.581 −4.619 −4.495 −4.400 −3.566 −3.556 −3.839 −3.788 −3.167 −2.927 −4.113 −3.803 −4.035 −4.061 −4.049 −4.085 −3.960 −3.844 −3.288 −3.278 −3.556 −3.498 −2.839 −2.590 −3.846 −3.515 −3.761 −3.789 −3.777 −3.816 −3.681 −3.563 lnChilet −1.001 [1] −9.812a [0] −3.541 [0] −6.706a [1] −2.155 [0] −4.978a [4] −3.266 [2] −9.468a [0] −3.326 [2] −7.625 [1] −3.242 [0] −5.065a [0] −3.069 [0] −6.989a [3] −1.984 [0] −5.528a [0] −1.872 [0] −5.813a [0] −1.736 [1] −10.986a [0] −3.728 [0] −7.076a [1] −1.639 [0] −4.990a [4] −2.728 [2] −8.801a [0] −3.086 [2] −7.576a [1] −3.117 [0] −4.746a [0] −2.679 [0] −6.973a [3] −2.254 [0] −4.875a [0] −2.277 [0] −5.165a [0] 0.602 0.753 0.849 0.825 0.706 0.943 0.718 0.900 0.792 0.719 0.984 0.870 0.627 0.470 0.856 0.972 0.811 0.995 1985 1986 1985 1985 1985 1980 2007 1982 1987 1984 1976 1985 1980 1982 2005 1995 2003 1995 – 2001 1999 1989 – – – 1993 1990 2004 1999 1999 1983 1986 – 1998 – 1998 −3.915 −4.468 −4.568 −4.543 −3.986 −4.164 −3.999 −4.620 −4.509 −4.434 −4.678 −4.590 −4.336 −4.113 −4.115 −4.670 −4.091 −4.196 −3.344 −3.927 −4.036 −4.012 −3.436 −3.630 −3.448 −4.086 −3.977 −3.884 −4.168 −4.057 −3.772 −3.538 −3.564 −4.156 −3.530 −3.667 −3.046 −3.648 −3.762 −3.736 −3.159 −3.359 −3.169 −3.817 −3.698 −3.605 −3.905 −3.785 −3.480 −3.231 −3.287 −3.892 −3.247 −3.399 −3.609 [0] −2.223 [0] 0.283 1985 1996 −3.784 −3.153 −2.825 −6.298 [1] −12.122a [1] 0.168 1985 1996 −3.492 −2.825 −2.484 Δ ln Chilet lnJapant Δ ln Japant lnMexicot Δ ln Mexicot lnNew Zealandt Δ ln New Zealandt lnPerut Δ ln Perut lnSingaporet Δ ln Singaporet lnVietnamt Δ ln Vietnamt lnWorldt Δ ln Worldt lnUTPPt Δ ln UTPPt lnRGDP2t ΔlnRGDP2t Due to the fact that the LM test and RALS-LM test similarly share the same process to search for the break points and the relevant optimal lags, we only report one time to conserve space The optimal number of lagged first-differenced term is reported in the parenthesis TB is the structural break point(s) The critical values are based on Akaike Information Criterion (AIC) The critical values of the LM test for two breaks are −4.689, −4.183 and −3.921 at the 1, and 10% levels, respectively The critical values of the LM test for one break are −4.199, −3.671 and −3.403 at the 1, and 10% levels, respectively All the critical values are computed, using the codes provided in https://www.dropbox.com/sh/dnjpjqmmgfi4otu/ AADNU7UVeqWjlNLxsoXn3gZWa?dl=0 For all the tests, the maximum lag is set at a 1% significance level b 5% significance level c 10% significance level Environ Sci Pollut Res Bounds test Table Model F stat Lag χ2serial χ2ARCH χ2Normal lnEMIt = f(lnRGDPt, lnURBt, lnTPPt, DUMt) 9.916c (1,4,3,0) lnEMIt = f(lnRGDPt, lnURBt, lnAUSTRALIAt, DUMt) 5.751b (1,0,4,1) lnEMIt = f(lnRGDPt, lnURBt, lnBRUNEIt, DUMt) 4.302a (1,0,4,3) lnEMIt = f(lnRGDPt, lnURBt, lnCANADAt, DUMt) 5.829b (1,0,3,2) lnEMIt = f(lnRGDPt, lnURBt, lnCHILEt, DUMt) 4.369a (1,3,3,0) 0.741 [2] 0.722 [1] 0.151 [1] 0.398 [1] 0.115 [1] lnEMIt = f(lnRGDPt, lnURBt, lnJAPANt, DUMt) 7.936c (1,0,2,2) lnEMIt = f(lnRGDPt, lnURBt, lnMEXICOt, DUMt) 4.237a (1,0,4,0) 0.136 [1] 0.579 [1] 0.580 [1] 0.265 [1] 0.689 [1] 0.716 [1] 0.301[1] lnEMIt = f(lnRGDPt, lnURBt, lnNEWZEALANDt, DUMt) 6.369c (1,0,4,1) lnEMIt = f(lnRGDPt, lnURBt, lnPERUt, DUMt) 5.261b (1,0,4,0) lnEMIt = f(lnRGDPt, lnURBt, lnSINGAPOREt, DUMt) 8.135c (1,0,3,0) 0.689 [1] 0.471 [2] 0.621 [2] 0.413 [2] 0.382 [2] 0.607 [2] 0.537 [2] 0.197 [2] 0.741 [2] 0.292 [2] lnEMIt = f(lnRGDPt, lnURBt, lnVIETNAMt, DUMt) 4.512a (3,0,0,0) 0.112 [2] lnEMIt = f(lnRGDPt, lnURBt, lnWORLDt, DUMt) 7.112c (1,0,3,1) lnEMIt = f(lnRGDPt, lnURBt, lnUTPPt, DUMt) 8.111c (1,0,3,0) À Á lnEMI t ¼ f lnRGDPt ; lnURBt ; lnTPPt ; lnRGDP2t ; DUM t 4.844b (1,0,3,4,1) 0.359 [1] 0.348 [1] 0.265 [1] 0.513 [1] 0.813 [1] 0.799 [1] 0.136 [1] 0.733 [1] 0.505 [1] 0.638 [1] 0.313 [1] 0.163 [2] 0.501 [1] 0.387 [1] 0.136 [1] 0.706 [2] 0.254 [2] 0.354 [2] 0.112 [2] For the four-variable models, the critical values (for lower and upper bounds) are (5.150, 6.280), (3.822, 4.714) and (3.226, 4.054), at 1, and 10%, respectively For the five-variable model, the critical values (for lower and upper bounds) are (4.628, 5.865), (3.470, 4.470) and (2.950, 3.862), at 1, and 10%, respectively The brackets show the order of diagnostic tests The specifications include unrestricted intercept and restricted trend The breaks included in the model is dummy for 1994 a 1% significance level b 5% significance level c 10% significance level Bera statistics suggest that all the variables follow normal distribution Unit root tests The RALS-LM unit root test is based on the following regression: square procedure which is used to estimate Eq Zt is a vector containing the exogenous series such that Z t ¼ Â Ã0 1; t; D*1t ; …; D*Rt ; DT *1t ; …; DT *Rt , where D1t∗ = for t ≥ TB + , i = , … , R, and 0, otherwise,DT *1t ¼ t−T Bi for t ≥ TB + 1, and TBi represents the location of the breaks and δ * are the coefficients in the regression of Δyt on ΔZt S~ t is the ~ t ~δ The transformed version of detrended series, S~t ¼ y Z t yt ẳ Z t ỵ S~ k * t1 ỵ d j S~t j ỵ w ^t ỵ ut; 2ị jẳ1 The null of the unit root is tested on ϕ = and the RALSLM statistic (τ × RALS ‐ LM) is generated via the normal least transformation is needed to remove the dependency of the test statistic on the nuisance parameter (see Lee et al 2012 for details) The distributions of τ~ Â RALS‐LM is identical to the untransformed case (~ τ RALS‐LM ) using the λ = 1/2,wt (contains the information on non-normal errors in a bid to Environ Sci Pollut Res further improve the strength of the LM statistic) is introduced into the model through the RALS approach.2 In the LM test of Lee et al (2012), γ = 0, and the t statistics for ϕ = is denoted by τ~*LM The lagged terms of ΔS t− j are introduced into the equations to ensure that there are no autocorrelations in the models ARDL bound test To employ the Autoregressive Distributed Lag (ARDL) bounds test approach of Pesaran et al (2001), the following unrestricted error correction model (UECM) is estimated: k k k i¼1 i¼0 i¼0 ΔlnEMI t ẳ ỵ lnRGDPti ỵ lnURBti ỵ lnTPPti EMI t1 ỵ RGDPt1 ỵ URBt1 ỵ TPPt1 ỵ Dt þ υt ð3Þ The null hypothesis of no cointegration (α5 = α6 = α7 = α8 = 0) is tested against the alternative hypothesis (α5 ≠ α6 ≠ α7 ≠ α8 ≠ 0) After analysing the long run relationship between the variables, we estimate the short run model as specified as follows: k k i¼1 i¼0 ΔlnEMI t ¼ α1 þ ∑ α2 ΔlnRGDPt−i þ ∑ α3 ΔlnURBt−i k þ lnTPPti ỵ Dt ỵ ECT t1 ỵ t 4ị iẳ0 records the adjustment parameter speed and ECT is the residual generated from the estimation of the cointegration equation in the long run models Finally, the stability of the models is analysed through the cumulative sum (CUSUM) and cumulative sum of squares (CUSUMSQ) tests Results The empirical analyses commence by testing the unit root features of the variables, which are reported in Table We implement the Meng et al (2014) unit root test in this study and, for comparison, we also report the test statistics of Lee et al (2012) method Consistent with the paper of Meng et al (2014), we set the maximum lag at and the approach mentioned by Dawson and Strazicich (2010) is utilised to determine the optimal lag In their original forms, nonstationarity is supported for all the variables When the variables are entered in their first differences, stationarity is supported for all the variables at 10% level It has been demonstrated that the asymptotic distribution of τ × RALS is pffiffiffiffiffiffiffiffiffiffiffiffiffi given as follows: τ*RALS−LM→ ρ~ τ * LM ỵ 12 Z where is the rel- ative ratio of the variances of two error terms or better It is observed that 27% of the structural breaks occurred in the latter part of 1990s, which is the period associated with the Asian financial crisis The crisis, which started because of speculative attacks on national currency of Thailand (Baht), spread to other neighbouring countries and affected not only the financial sector but also real sector in Malaysia Domestic-oriented sectors such as construction and services sectors were also adversely affected Malaysia experienced the biggest plunge in the region as stock market capitalization decreased by about 76% Therefore, several sectors, including the construction and services industries, were harshly affected by the financial crisis (Ariff and Abubakar 1999) After observing the integration properties of the series, we proceed with the ARDL test to examine potential long run relationship in the series In Table 4, we report the findings of different set of equations In the first model, we report the findings of an equation involving CO2 emission (as the dependent variable), real GDP, urban population ratio and real trade per capita with TPP members The evidence suggests that there is cointegration as the F statistics (9.916) is greater than the upper critical value (6.280) at 1% significance level We further examine the cointegration while using the real trade per capita in each TPP members as proxies for trade openness It is observed that we cannot accept the null of no cointegration when the dataset of each TPP country is utilised as proxy for trade openness Furthermore, we also test for cointegration, when the total trade for all trading countries and TPP members plus the USA are used The F statistics in the equation involving all trading countries (7.112) and the F statistics in the equation involving TPP countries plus the USA (8.111) are bigger than the upper critical value at (6.280) 1% significance level Lastly, we examine the possibility of cointegration in an equation involving CO2 emission (as the dependent variable), real GDP, real GDP square, urban population ratio and real trade per capita with TPP members The evidence suggests that there is cointegration as the F statistics (4.844) is greater than the upper critical value (4.470) at 5% significance level The diagnostic tests indicate that there is no problem of serial correlation, heteroscedasticity and nonnormality In Table 5, we estimate both the long run and short run coefficients and the focus is on the equations with evidence for cointegration In the first model, we report the findings of an equation involving CO2 emission (as the dependent variable), real GDP, urban population ratio and real trade per capita with TPP members We observe that real GDP, urban population ratio and real trade per capita with TPP members have positive and significant impact on CO2 emissions at 10% significance level or better In model to model 11, we replace real trade per capita with TPP members with real trade per Dummy 1994 Trend Constant lnRGDP2t lnUTPP lnWorldt lnVietnamt lnSingaporet lnNew Zealandt lnPerut lnMexicot lnJapant lnChilet lnCanadat lnBruneit lnAustraliat lnTPPt lnURBt Model 0.237a (2.694) 4.729a (6.035) 9.933a (3.780) Model 0.581b (2.332) 4.462a (5.602) 6.621b (2.478) Model 0.087 (1.590) 2.508a (2.599) 15.587a (5.300) Model 1.832c (1.898) 0.1667a (2.616) 2.631b 4.269a (2.125) (6.027) −4.115 9.011a (−0.696) (3.472) 0.506b (2.375) 3.781a (5.031) 10.469a (3.949) Panel A: long run coefficients Model Model Model 0.012 (0.180) 3.919a (4.239) 10.542a (3.440) Model 0.505b (2.195) 3.558a (4.957) 8.072a (3.386) Model 10 0.059 (0.596) 3.101a (2.843) 3.471 (1.092) Model 11 1.009a (3.354) 3.911a (6.003) 3.963 (1.438) Model 12 Model 14 0.830a (2.926) 3.939a 0.854b (5.763) (2.061) 4.762c 6.532a (1.750) (3.653) 0.140 (0.888) Model 13 0.081b (8.85) −68.602a −55.394a −72.231a −9.087 −61.592a −63.136a −65.387a −55.157a −35.002a −46.167a −48.000a −29.161a −48.000a −65.911a (−4.96(−6.692) (−6.716) (−5.692) (−6.956) (−0.412) (−6.318) (0.000) (−5.727) (−5.829) (−3.149) (−4.726) (−4.96(−5.478) 5) 5) −0.230a −0.177a −0.274a 0.051 −0.222a −0.217a −0.221a −0.182a −0.059 −0.130b −0.145a −0.218a −0.074b −0.145a (−2.73(−3.759) (−3.828) (−3.203) (−4.186) (0.475) (−3.884) (−3.576) (−3.303) (−3.444) (−0.798) (−2.396) (−2.73(−2.496) 9) 9) 0.115a 0.155a 0.165a 0.219a 0.136a 0.135a 0.144a 0.168a 0.132b 0.153a 0.178a 0.125a −0.059a 0.178a (10.08(7.980) (6.935) (10.314) (8.936) (4.334) (10.299) (9.540) (9.509) (9.341) (3.839) (12.496) (10.08(−2.710) 3) 3) Panel B: short run coefficients 4.446a 4.134a (6.546) (5.993) 4.862b 9.397a (2.053) (3.605) 0.837a (3.650) 0.572b (2.248) Model Determinants of CO2 emissions in Malaysia Independent variable lnRGDPt Table Environ Sci Pollut Res −45.816a (−4.452) −0.138a (−2.711) 0.169a (6.671) −0.955a (−7.749) 0.642 4.438a (5.865) 39.822b (2.158) 3.760a (5.871) 28.644c (1.664) 0.792a (2.954) −65.800a (−5.296) −0.218a (−3.485) 0.125a (4.988) −0.998a (−7.170) 0.589 0.532c (1.945) Model Model −66.419a (−5.252) −0.223a (3.500) 0.112a (4.323) −0.968a (−7.053) 0.641 0.082 (1.129) 4.579a (6.9319) 17.939 (0.927) Model3 −52.747a (−4.942) −0.169a (−3.157) 0.147a (5.896) −0.952a (−7.038) 0.587 0.207 (0.802) 4.249a (5.628) 24.920 (1.303) Model −66.702a (−5.311) −0.253a (−3.931) 0.152a (4.904) −0.923a (−5.606) 0.661 0.0800 (1.555) 4.430a (5.692) 40.532a (2.312) Model −6.130 (−0.406) 0.034 (0.476) 0.148a (4.215) −0.675a (−4.866) 0.421 0.489 (1.201) 1.774b (2.130) 27.480 (1.295) Model −60.429a (−5.117) −0.218a (−3.620) 0.134a (5.701) −0.981a (−7.393) 0.622 0.164a (2.635) 4.188a (6.033) 24.916 (1.333) Model −60.620a (−5.023) −0.207a (−3.382) 0.129a (5.302) −0.955a (−6.817) 0.606 0.376c (1.865) 3.610a (5.245) 25.594 (1.299) Model −60.831a (−4.641) −0.206a (−3.068) 0.134a (5.047) −0.930a (−6.308) 0.535 0.012 (0.179) 3.646a (4.476) 40.184b (2.026) Model −53.927a (−5.216) −0.178a (−3.424) 0.165a (5.716) −0.978a (−7.259) 0.599 0.492b (2.097) 3.478a (0.679) 37.684b (2.103) Model 10 −28.456a (−2.824) −0.048 (0.816) 0.107a (2.585) −0.813a (−3.498) 0.326 0.048 (0.591) 0.109 (0.540) 2.822 (1.021) Model 12 −45.504a (−4.330) −0.128b (−2.385) −0.151a (7.040) 0.986a (−8.132) 0.6611 0.712b (1.961) 3.855a (6.091) 22.346 (1.292) Model 13 −45.816a (−4.452) −0.138a (−2.711) 0.169a (6.671) −0.955a (−7.749) 0.642 0.792a (2.954) 3.760a (5.871) 28.644c (1.664) Model 14 0.082a (15.176) −21 001a (−3.428) −0.053b (−2.097) −0.043a (−2.253) −0.720a (−4.839) 0.934 0.615b (2.084) −3.014 (−0.606) 0.101 (0.884) Model 15 R2 Diagnostic test Test Probabilit- Probability Probability Probability Probability Probability Probability Probability Probability Probability Probability Probability Probabilit- Probability y value value value value value value value value value value value value y value value 0.878 [1] 0.818 [1] 0.662 [1] 0.113 [1] 0.870 [1] 0.581 [1] 0.735 [1] 0.357 [1] 0.925 [1] 0.312 [1] 0.878 [1] 0.266 [1] 0.775 [1] 0.111 [1] χserial Dummy 1973 ECT t−1 Trend Constant ΔlnRGDP2t Δ ln Singaporet Δ ln Vietnamt Δ ln WORLDt Δ ln UTPP Δ ln New Zealandt Δ ln Perut Δ ln Mexicot Δ ln Japant Δ ln Chilet Δ ln Canadat Δ ln Australiat Δ ln Bruneit Δ ln TPPt Δ ln URBt Independent variable Δ ln RGDPt Table (continued) Environ Sci Pollut Res capita in each TPP member as a proxy for trade openness The results show that there is positive impact of real trade per capita for all the TPP members on emission in Malaysia However, the relationship is insignificant in the case of Chile, Peru and Vietnam There is positive and significant influence of real GDP and urban population ratio in most cases The results involving real trade per capita of all trading countries and real trade per capita of TPP countries plus the USA as proxies for trade openness are presented in model 12 and model 13, respectively The results show that two trade proxies have positive and significant impact on emission in the country Lastly, the environmental Kuznets curve (EKC) hypothesis is investigated in model 14 The results show that both real GDP and real GDP square are positive and significant, which implies that there is no EKC in the country In most of the foregoing equations, the dummy has positive impact on CO2 emissions The results are not materially different in the short run The error correction terms in all the equations suggest that the disequilibrium in the previous year is corrected in the current period Alternatively, this means that there is long run link among the variables, thus rendering our long run estimates valid The CUSUM and CUSUMSQ tests largely support stability of the coefficients of the regression equations For the sake of robustness, the ARDL long run outputs are augmented with three different estimates of the baseline regressions involving CO2 emission (as the dependent variable), real GDP, urban population ratio and real trade per capita with TPP members in Table Firstly, we implement the fully modified ordinary least square (FMOLS) of Phillips and Hansen (1990) Secondly, we use the Johansen et al (2000) cointegration test which incorporates the deterministic components and exogenous several structural shifts in the model In the current paper, we provide for single structural break (which is located in 1994 and it is derived from the unit root test of the dependent variable, CO2 emission) in the estimation Before generating the long run results, we have conducted the cointegration test and the results suggest the existence of one cointegrating vector Lastly, we implement the Hatemi-J (2008) test, which provides for double structural breaks Unlike Johansen et al (2000), which provides for exogenous structural breaks, the Hatemi-J (2008) test has the advantage of providing for double endogenous structural breaks Before generating the long run results, we have conducted the cointegration test through the procedure provided Significance at 5% Significance at 1% Significance at 10% c b a The optimal lag length is determined by Akaike Information Criterion The parenthesis contains the t statistics while the bracket contains the order of diagnostic tests R2 is the adjusted R2 Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable Stable 0.480 [2] 0.224 [2] 0.394 [2] 0.173 [2] 0.221 [2] 0.777 [2] 0.427 [2] 0.358 [2] 0.451 [2] 0.212 [2] 0.760 [2] 0.910 [2] 0.213 [2] 0.224 [2] χ2Normal CUSUM CUSUMSQ 0.107 [1] 0.567 [1] χ2ARCH Table (continued) 0.387 [1] 0.274 [1] 0.109 [1] 0.821 [1] 0.559 [2] 0.431 [1] 0.553[1] 0.198 [1] 0.379 [1] 0.979 [1] 0.567 [1] 0.817 [1] Environ Sci Pollut Res The results of the cointegration tests are not presented here but can be provided upon enquiry The results of the cointegration tests are not presented here but can be provided upon enquiry The data are available for the period 1970–2012 and collected from the World Development Indicators of the World Bank Environ Sci Pollut Res by this test and the results suggest the existence of cointegration.4 Following the work of Solarin and Lean (2016), the coefficients reported involve adding the level and the two dummy estimates of each variable The foregoing estimates suggest that real GDP and real trade per capita have positive impact on emission CO2 emission is the core source of emissions in the country However, there are other forms of total greenhouse gas emissions in Malaysia According to world development indicators of the World Bank, the total greenhouse gas emissions in the country was 279,098 (kilotonnes of CO2 equivalent) and CO2 emissions accounted for 81% of total emissions in 2012 Methane emissions and nitrous oxide emissions accounted for 12 and 5% of the total emissions in the same year, respectively In Table 7, we conduct another robustness analyses by estimating the impact of real GDP per capita, real trade per capita of TPP members and urban population ratio on the three categories of emission lnEMIt is the CO2 emissions (metric tonnes) per capita, lnMEMIt is methane emissions (tonnes of CO2 equivalent) per capita and lnNEMIt is nitrous oxide emissions (metric tonnes of CO2 equivalent) per capita.5 Thus, we are able to check again the relationship while using another dataset and we are able to look at the impact of the independent variables on different types of emissions in the country The results show that real trade per capita of TPP members has positive and significant impact on CO2 emissions and methane emissions but positive and insignificant impact on nitrous oxide emissions Discussion In summary, it is shown that real GDP has positive impact on emissions, which is consistent with the works of Al-mulali and Ozturk (2016), Bento and Moutinho (2016), Charfeddine and Khediri (2016) and Haq et al (2016) Moreover, urban population ratio has positive impact on CO2 emissions, which is consistent with the results of Al-mulali and Ozturk (2016), Dogan and Seker (2016) The results further show that there is a positive effect of real trade per capita with TPP members, real trade with all countries per capita and real trade with TPP countries plus US per capita The studies with similar positive impact on emission include Al-mulali et al (2016a), Bento and Moutinho (2016) and Ertugrul et al (2016) The positive impact of real GDP is consistent with the expectation of the STRIPAT theory, which has hypothesised that affluence will lead to more emission The positive impact of real GDP can be explained on the basis that the expansion of the economic activities requires The results of the cointegration tests are not presented here but can be provided upon enquiry The data are available for the period 1970–2012 and collected from the World Development Indicators of the World Bank various forms of energy as inputs Over the years, both emissions and real GDP have been experiencing positive growth rate According to BP Statistical Review of World Energy, emissions increased by an average of 7.9% in the period of 1970–2014 According to world development indicators of the World Bank, real GDP increased by an average of 6.3% in the period of 1970–2014 In Malaysia, fossil fuels are known to be responsible for emission as they dominate the energy mix According to BP Statistical Review of World Energy, fossil fuels accounted for about 97% of the total primary energy consumed in 2013 and 2014, respectively Natural gas consumption (which amounted to 36.3 million tonnes oil equivalent) accounted for 40% of the total primary energy consumed, oil consumption (which amounted to 34.5 million tonnes oil equivalent) accounted for 38% of the total primary energy consumed and coal consumption (which amounted to 17.0 million tonnes oil equivalent) accounted for 18.7% of the total primary energy consumed in 2013 Natural gas consumption (which amounted to 36.9 million tonnes oil equivalent) accounted for 41% of the total primary energy consumed, oil consumption (which amounted to 35.2 million tonnes oil equivalent) accounted for 39% of the total primary energy consumed and coal consumption (which amounted to 15.9 million tonnes oil equivalent) accounted for 17% of the total primary energy consumed in 2014 Moreover, almost 80% of Malaysia’s emissions are from its energy related sectors, including power generation, transport, industrial, residential and commercial sectors (Colenbrander et al., 2016) The positive impact of urbanisation is consistent with the theory of STRIPAT The impact of urbanisation on emissions is not surprising given that urbanisation is associated with industrialization, which is more energy-intensive than the agriculture sector In Malaysia, over 90% of productive activities are undertaken in cities in the country (Colenbrander et al 2016) Transport and industrial sectors are the largest consumer of energy in urban areas in Malaysia, generally accounting for 50– 60% (Shahbaz et al 2016d) Energy consumption and pollutions are therefore likely to be concentrated in cities (Colenbrander et al 2016) The findings on the impact of the various forms of indicators of trade openness are not consistent with idea of STRIPAT, which assumes that many variables including trade openness will be associated with transfer of positive technology However, this finding follows the claim of the pollution haven hypothesis, which suggest that trade liberalisation will ensure multinational companies in the developed economies, where environmental regulations are strict, will move their pollution intensive factories to developing countries where environmental standards are slack and would increase make emissions to increase in the host countries.6 The positive impact of the trade The developed countries among the TPP members include Australia, Canada, Japan, New Zealand and Singapore Environ Sci Pollut Res Table Determinants of CO2 emission in Malaysia and alternative methods Independent variables lnRGDPt lnURBt lnTPPt Method FMOLS Johansen Hatemi-J 1.702b (2.052) 3.322c (3.543) 3.554c (3.449) b 1.512 (2.445) 0.343b (2.410) b 2.253 (2.433) 0.227c (4.000) −22.659 (−0.477) 23.443a (1.433) The parenthesis contains the t statistics For the Hatemi-J, the results reported include sum of level and the two dummy estimates a Significance at 10% b Significance at 5% c Significance at 1% with TPP members on emissions can be explained on the basis that fossil fuels dominate production activities in the country Another reason for the positive impact is that manufactured products, which are known to be associated with emissions, dominate the Malaysia’s exports Manufactured products are vital contributor to the emissions in several nations Manufactured products are traded universally and their contribution is frequently not evaluated properly due to their complex supply chains A large part of the pollution attributed to manufacturing happens in emerging economies with enormous emission intensity, while the consumption of these goods occurs mostly in rich economies (United Nations Environment Programme 2010) According to Malaysia External Trade Development Corporation (2016), the total exports was valued at RM765.42 billion in 2014 whereby electrical and electronic products (with a value Table Determinants of different pollutants in Malaysia Independent variables lnRGDPt lnURBt lnTPPt Constant Trend Dummy 1983 Dummy 1989 Dummy 1998 of RM254.14 billion) accounted for 35% of the total exports The pollution generated in the manufacturing sector to produce these electronic equipment is identical to or larger than the pollution caused by their electricity consumption (Hertwich and Roux 2011) Manufactured products including electrical, electronics, metal, optical and scientific equipment account for large portion of the total trade in each of the TPP members (Malaysia External Trade Development Corporation 2016) Previous trade deals have caused an extractive mode of globalisation that has led to enormous deforestation, fossil fuel withdrawal and an energy-intensive industrial model of agriculture (Rimmer 2016) The insignificant results from the findings of Chile, Peru and Vietnam are not surprising given the fact that they are among the TPP countries with the smallest volume of trade with Malaysia Dependent variable lnEMIt lnMEMIt lnNEMIt 0.976c (3.928) 5.402c (3.068) 0.505c (3.352) 9.706 (1.589) 0.098c (3.062) 0.691c (3.919) 4.252c (4.048) 0.236b (2.489) −19.080c (5.214) −0.094c (−4.835) 0.223b (2.083) −1.112 (−1.080) 0.155 (0.831) 1.901 (0.562) 0.050c (2.715) −0.050c (−8.837) 0.006 (1.169) −0.007 (−1.276)) The parenthesis contains the t statistics The dummies added are derived from the unit root tests of the dependent variables at level b Significance at 5% c Significance at 1% Environ Sci Pollut Res Conclusion This study examined the link between trade with TPP members and CO2 emission in Malaysia for the period of 1970– 2014 Within the STRIPAT framework, we provided for real GDP and urbanisation The impacts of trade with each TPP members, trade with all countries and trading with TPP countries plus the USA on emissions were also examined We further performed sensitivity analysis by looking at the impact of trading with TPP members, real GDP and urbanisation on methane emissions and nitrous oxide emissions in the country We augmented the conventional ARDL approach with structural break to investigate the long run link between the variables Our results indicated that trading with TPP members, real GDP and urbanisation, trading with all countries and trading with TPP countries plus the USA have positive impact on CO2 emissions The results further showed that trading with TPP members has a positive impact on methane emissions The meaning of these findings is that trade openness is contributing to the growing emission level in the country Trading with TPP members has been contributing to emissions in the country, and if the status quo remains, more trading with these countries will lead to more emissions in the future The foregoing results not necessarily suggest a call for the cancellation of the TPP agreement because the country has a small market and, therefore, depends on exports Right from the early 1970s when the export oriented strategy started, the country has continued to be export-dependent economy It is not also a call for the government to increase trade with non-TPP members at the expense of the TPP members as the evidence has shown that trading with all countries has positive impact on emissions The importance of TPP in context of Malaysia is crucial as it includes many important trading partners which account and 30% of Malaysia’s total trade in the year 2015 (Banga and Sahu 2015) TPP would increase the GDP and the welfare of majority of the TPP member countries, particularly for the small economies (Todsadee et al 2012) Therefore, it would be unwise for the country to use trade protection as a means of improving its environment because this would adversely affect its exports to both TPP and non-TPP members According to the world development indicators of the World Bank, the ratio of exports (of goods and services) to GDP was almost 74% in 2014 All the 12 National Key Economic Areas (NKEAs) are all associated with international trade Moreover, incentives should be This figure excludes the USA The 12 National Key Economic Areas (NKEAs) comprise selected sectors of economic opportunity for the private sector which will drive Malaysia towards high-income status and global competitiveness introduced to encourage environmentally friendly technology transfer from TPP members, especially from the developed ones As Malaysia is still dependent on imported capitals and intermediate goods (Ibrahim and Rizvi 2015), perhaps a requirement or incentive should be adopted to encourage the imports of more environmentally friendly technologies In short, support and regulatory mechanisms, such as incentives and tax holidays, for the adoption of environmentally friendly technologies are required Another recommendation is that Malaysia should increase the share of renewable energy in the energy mix of the country Currently, renewable energy accounts for less than 3% in the primary energy mix In achieving this aim, the country should try to diversify renewable energy as hydroelectricity dominates the renewable energy mix (which implies that renewable energy is predominantly used for electricity purpose) For example, solar energy, biomass and tidal energy are potential renewable energy since the country possesses abundant natural resources Similar policies have been recently introduced The Malaysian government offers feed-in-tariff (FITs) for small hydro, solar photovoltaic (PV), biomass and biogas The FIT scheme has been particularly successful in increasing solar PV deployment (International Energy Agency 2016) To realise sustainability, prominence must be given to the use of clean technology so that the negative effects on the environment can be decreased Another suggestion is the use of nuclear energy It is very suitable for a large-scale, stable supply of electricity and does not emit any emission to generate power Besides, the introduction of alternative sources of energy and the introduction of abatement technologies, carbon emission permits and carbon taxes can be applied to reduce emissions in Malaysia The study is not without its limitations First, we have not considered the role of the potential members of TPP, including Laos, Indonesia, Philippines, India, Cambodia and Thailand Some of these 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