Spotting the Environmental Effect of the Economy and Technology: How the Development is Causing a Stringency with Carbon Emission ?

The increasing threat to natural climate has extended the focused on economic growth and technology. Thus, this research contribution has provided an empirical investigation for examining the impact of economic and science and technology indicators on carbon emission from five different sources namely; emission from transport industry, other sectors, residential building, electricity and heat production, and emission from manufacturing industries in Thailand. Different statistical analyses were done to analyze the individual and combine effect of both economic and science and technological indicators during 1990-2014 with yearly data trends. The results show that some economic factors are found to be positive determinants of carbon emission, while others have shown their adverse influence in increasing carbon threats to natural environment in Thailand. As per the research implications, present work is among the initial contribution while exploring the environmental effects of growth and technology in Thailand for which a specific research findings are presented earlier. For this reason, this work can provide a good understanding to policy-makers, reserachers, and students in the targeted fields like carbon emission, economy and science and technology. Moreover, some productive future directions are also provided in this paper. First, methodological context of the study can be revised for better findings through some time series models. Second, regional context of the study may spread to other economies like Indonesia, Malaysia and Singapore.


INTRODUCTION
In current era, a range of economic and technological developments are experienced by the humans in the world. However, at the same time, such developments are causing a range of challenges because of their environmental effects in both developed and developing countries (Åström et al., 2012;Béguin et al., 2011;Kamran and Omran, 2018;Prowse et al., 2009;Santana Vilorta, 2020;Santos-Munguía and Perez, 2020). Since the deterioration of the natural environment, there is a growing concern about the climate change and global warming (Cox et al., 2000;McCright and Dunlap, 2011;Mori et al., 2010;Whitmarsh, 2009). The reason behind such threats to nature and the whole planet earth is observed in the form of increasing emission of carbon from different sources and sectors (Huang et al., 2018;Liu et al., 2019;Shao et al., 2018). Although, a range of financial and economical benefits are enjoyed by the world community due to advancement in technology, however, environmental degradation is also an outcome of such development (Kutz, 2018;Moreno-Brid et al., 2020;Ormaechea and Fernandez, 2020;Ortiz and Castillo Renteria, 2020). It is known as the deterioration of the natural climate due to depletion of range of sources like water air, and most importantly, the destruction of overall ecological system (Demirbas, 2005;Goudie, 2018;Peterson, 2019;Aleem, 2020;Batool et al., 2020;Hornung, 2020;Janssen, 2020). More consumptions of natural resources result in devastation of the environment. Various types of environmental degradation are observed and some of them are in critical situation which needs immediate solution from the world leaders and departments like United Nations (UN).
In any region of the world, the factor of economic development depends on variety of the indicators. Different mechanism are employed by diverse economies with the usage of natural sources. However, many aspects are highlighted in the literature, covering the fact that more development in any country results in adverse impact on the environment like pollution, more usage of the resources, creation of carbon emission in the climate, and similar other threats (Cumming and von Cramon-Taubadel, 2018;Danish and Wang, 2018;Longo and Baker, 2014;Perera, 2018;Singh et al., 2013;Martínez-Martínez, 2020;Nguyen et al., 2020;Nurpeisova et al., 2020). For this reason, various economies are facing the issue of low quality of natural environment and bad living standards for its communities too. As inspired from this issue, present paper has highlighted the situation of carbon emission in the economy of Thailand while investigating its relationship with different economic indicators and science and technology factors. After the detailed investigation of Thai economy, it is found that many issues are presented in terms of natural environment like air pollution through carbon emission, soil erosion, water pollution, damage to eco-system and deforestation as well. As per Figure 1 below, it is found that carbon emission from heat and production (CEHP) is significantly greater than the rest of the indicators, showing a significant portion (yellow color). Whereas the carbon emission from transport (CTR) has also shown an alarming situation. Whereas, carbon emission from manufacturing industries (CMI) seems to cover the third ranking as observed in the Figure 1.

LITERATURE REVIEW
There are number of theoretical and empirical studies on the topic of carbon emission in different regions. This section covers some of the studies for the relationship between carbon emission, economic growth and technological factors too. For example, Aye and Edoja (2017) investigated the impact of economic growth on carbon emission while using the panel threshold framework, based on the data from 31 emerging economies. It is found that economic growth is negatively influencing the CO 2 emission in the low growth economies. More specifically, in high growth economies, the impact of economic growth on carbon emission is positively significant. Additionally, their findings have provided no evidence to support for the environmental Kuznets curve (EKC), rather they have found a u-shaped relationship between the selected variables. In addition, their study has found the causal relationship between carbon emission, economic growth and financial development. Schröder and Storm (2018) aims to investigate the restriction to stop the future global warming with the measurement of carbon emission. Expanding their discussion, authors claims that literature work is also arguing about radical de-carbonization with the increasing type of the economy. Mardani et al. (2019) has conducted a systematic review for the relationship between carbon emission and economic growth over the last two decades. A review from 175 articles have revealed the fact that there is bidirectional causality between the economic growth and emission of carbon in different economies.
In addition, Pandey and Rastogi (2019) empirically examined the impact of energy consumption in terms of electricity and its relationship with GDP under environmental degradation during 1971-2017. As per the trends of the data set, time series analyses like Dicky Fuller test has been applied to investigate the short term and long term relationship between GDP and carbon emission with co-integration and Granger Causality methods. As per the applied methods, authors found that there is a long-term relationship between the defined variables. However, short run causality exists between electricity consumption to economic growth and carbon emissions. Ismail (1996) has investigated the Malaysian economy in terms of economic evaluation of the carbon emission in the forestry sector. Galeotti et al. (2017) has measured the environmental policy stringency for the impact of environmental innovation and efficiency factor of the energy too. Authors claim for the contribution in the literature through computing the diversified indicators of environmental policy stringency with the testing of environmental innovation.

RESEARCH METHODS AND VARIABLES
Based on the above literature, authors have investigated the causal effect of economy and science on carbon emission, considering the following variables of the study. • Gross domestic product: measured through annual growth % (GDPCG) as independent variable I (Djankov et al., 2006 (Seyoum, 2004).
The data for the above explanatory and outcome variables is purely secondary in nature and collected from different sources during 1990-2014 with annual observations. This study has applied the multiple regression methods to investigate the causal relationship between economic factors, scient and technology indicators and carbon emission through five major sources. Equation 1 below provides the information about dependent and independent variables, regression coefficients, and error terms.
Carbon emission=b0+b1͠ ...b4(economic factors)+b5͠ ...b7(science and technology)+e Where carbon emission is measured through CTR, COS, CMI, CEHP, and CRBC respectively under sperate regression models. Meanwhile, to analyze the effect from economic factors, key titles are: GDPCG, GDPPC, GDPPCC, and GDPPUSD respectively as presented through regression coefficients of b1-b4. Additionally, effect from the factors of science and technology key titles are: CUIP, R&D, and HTEXPUSD and their marginal impact on carbon emission is reflected through b5-b7. Besides, this study also observed those factors which are not included in the model but entitled as error terms (e).

CTR
--------------::::::::::::::::::::::::::::::::: GDP per capita (current LCU), and GDP per capita (current US$) with the average trends of 1.10E+05 and 3219.47 in US dollars and deviation of 47393 and 1482 respectively (Hussain et al., 2020;. After discussing the descriptive framework of both carbon emission proxies and economic factors of development, present discussion is providing a review about science and technology factors. To address this objective, three factors entitled as Charges for the use of intellectual property, payments (BoP, current US$) or CUIP, Researchers in R&D (per million people) or R&D, and High-technology exports (current US$) or HTEXPUSD are added among other regressors of the study. It is found that for these set of the variables, R&D is showing a mean trend 329.74, whereas rest of the two factors have shown an average score of 1.60E+09 and 3.30E+10 during the period of study.
The descriptive findings have provided a good understanding to examine the layout of the data for selected variables over the period of study. Furthermore, robust coefficients through regression analyses show that for the First measure of carbon emission as observed through CTR, there is a significant and negative influence from GDPPC where the coefficients and standard error are providing a P < 1%, hence accepting the negative and significant impact of GDPPC on CTR during the study period. This would explain that with the more GDPPCC int eh economy of Thailand, lessor the carbon emission from transportation and similar other sources. This result providing an evidence for the policy-makers dealing with the climate change and economic growth factors. On the other hand, GDPPUSD shows an effect of 0.002211 on carbon emission from transportation, defending that more the GDP per capita in $, more the carbon emission from the transportation. This relationship is saying that attention is required to mitigate the increasing trends of CTR from GDP per capita ($). The rest of the economic factors are found to be insignificant indicators of CTR in Thailand.
Moving towards the factors for the science and technology, only the effect from R&D is positive, significant as t-value found to be above the threshold point of 1.96. This could say that one of the key reasons for increasing CTR in Thailand is more R&D. Whereas, no impact of CUIP and HTEXPUSD on CTR is found.
The third step under Table 2 provide the combine effect of economic factors and science and technology on carbon emission from transportation. Furthermore, the model findings showing negative effect from GDPCG, and GDPPCC, provided that higher such economic factors, the lower the CTR which is indeed a good sign. This relationship is a good indication in the Thailand economy as much growth for GDP per capita, and GDP per capita in the form of current LCU are helping to secure the natural environment with lowering the carbon emission of transport.
For second indicator of carbon emission as reflected through emission from other sectors in Thailand. For analyzing the relationship between economic factors and science and technology as determinants of carbon emission, similar steps are repeated as those which are considered for the carbon emission from transport. Column five, Table 2 shows that significant and negative impact from GDPPC and GDPPUSD is found. Their effect is highly significant at 1%, with lowest value of p-score. It would justify that higher the economic development like GDPPC and GDPPUSD, lower the carbon emission from other sectors in Thailand. While the influence from GDPPCC is found to be positive and significant at 1%. This means that higher the economic growth like GDPPCC, higher the carbon emission is experienced from other sector.
After the discussion for the influence of economic factors on carbon emission from other sector, column six shows a highly significant and negative influence from HTEXPUSD; -0.3674  (0.0525), accepting the fact that higher the HTEXPUSD, lowers the carbon emission as vice versa. Last column In Table 2 shows the combine effect from economic factors and science and technology indicators. As per the findings, only the GDPPC and HTEXPUSD are showing that they are causing a reduction in the carbon emission from other sectors. Besides, Figure 2 provides the general understanding for the regression outlier graphs as observed for each of the explanatory variables of the study.
For examining the carbon emission trends through manufacturing industries and impact from economic factors and science and technology, Table 3 is showing the regression results. As per the findings under column 2, significant and positive, and significant and negative impact on carbon emission through manufacturing industries is found. This would further justify through coefficients as per robust standard errors, significant at 1%. It means that higher GDPPC is causing more carbon emission from manufacturing in Thailand, While GDPPCC is causing a reduction in carbon emission from manufacturing sector. For the indicators of science and technology, CUIP is lowering the carbon trends from manufacturing sector, whereas HTEXPUSD is causing an increasing trend in such emission in Thailand. However, the combine effect from both economic and science and technology indicators, only the factor of HTEXPUSD is found a direct determinant for carbon emission from manufacturing sector.
For carbon emission from electricity and heat production, Column five (Table 3) presents the evidence for the significant and negative determinant effect from GDPPC and GDPPUSD respectively. These factors are justifying that with the more such growth trends, lower the carbon emission from electricity and heat production. Contrary to this, the impact from GDPPCC is positive and significant at 1%, saying that more the GDDPPCC higher the carbon emission with the consumption of electricity and heat productions.
The impact from CUIP on CEHP is highly positive, causing more issue such carbon emission in natural environment. More importantly, the combine effect of both economic and science and technology indicators, positive impact from GDPCG, GDPPC, while negative from GDPPC, GDPPUSD, R&D, and HTEXPUSD. For the last factor of carbon emission from residential buildings and commercials, all economic factors are found as significant determinant when their separate effect is examined. However, HTEXPUSD as an indicator of science and technology is causing more such emission (Column 9, Table 3). In the end, when the combine effect is presented (Column 10), positive impact from GDPPUSD and HTEXPUSD, while negative from CUIP is expressed.

CONCLUSION AND RECOMMENDATIONS
Environmental outcomes of economic development and technology are reasonably influencing the decision makers in the field of climate change. A common notion is that higher economic growth and innovation in science and technology has a mixed trend on nature in the form of carbon emission through different sources. Increasing concerns about the environmental adverse impact through climate change and global warming are putting more pressure on the governments to take some serious steps. To avoid from such issues, world economies are implementing the environmental regulations, personal carbon trading, low carbon emission through travel schemes, and many other policies are highlighted in the literature. However, the relationship between economic factors, science and technology and carbon emission in the economy of Thailand is not well addressed. Therefore, this study has observed the environmental outcomes of economic and science and technology indicators. For examining the economy, overall four factors; GDP per capita growth as annual percentage, GDP per capita constant in USD, GDP per capita in current LCU, and GDP per capita current USD were added in the regression models. Whereas science and technology head is further explained through charges for the use of intellectual property, research and development, and high technology exports in current USD are added in the models. To represent the natural impact, carbon emission from transport, other sector, manufacturing industries, electricity, heat production, and carbon emission from residential buildings were considered as main dependent variables of the study.
A descriptive look has provided a good understanding for all the variables along with standard deviation. As per empirical findings, economic factors like GDPPCC and GDPUSD are found to be negative and positive determinant for the carbon emission through transport when their effect is observed without considering the science and technology. This effect shows that GDPPCC is causing a reduction in carbon emission from transport, whereas GDPUSD is causing more emission from transport. From science and technology, R&D is found to be a direct indicator of carbon emission in Thailand from overall transport industry. When the combine effect from economic and science and technology factor is observed. GDPCG, GDPPC, and GDPUSD are showing that they are causing a reduction during the study period. In the second, step, significant determinant for the carbon emission from other sectors are; GDPCG, GDPPCC, and GDPUSD. In case of science and technology, HTEXPUSD has its adverse influence on the carbon emission from other sectors. However, under combine effect model, key indicators for the carbon emission are GDPPC, and HTEXPUSD only, showing the declining trend. For the remaining factors of carbon emission (from manufacturing industries, electricity and heat production, residential building and commercials) it is observed that GDPPCC is showing both positive and negative influence, whereas CUIP is found to be a direct determinant for carbon emission from manufacturing industries and carbon emission from electricity heat and production etc. In the last, HTEXPUSD has demonstrated the evidence that more emission from manufacturing industries, and residential building due to such high-tech exports in Thailand.
Besides, this paper is observed as a good contribution specifically in the economy of Thailand for exploring the effect of economic development and science and technology factors in determining the carbon emission. The presented analysis has provided enough evidence, showing that which economic and science and technology factors need to investigate as an opportunity or threat for the changing climate change in terms of increasing carbon emission. Additionally, some fruitful future directions are also provided in this paper. First, methodological context of the study can be revised for better findings through some time series models. Second, regional context of the study may spread to other economies like Indonesia, Malaysia and Singapore.