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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Environ. Sci.</journal-id>
<journal-title>Frontiers in Environmental Science</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Environ. Sci.</abbrev-journal-title>
<issn pub-type="epub">2296-665X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">852379</article-id>
<article-id pub-id-type="doi">10.3389/fenvs.2022.852379</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Environmental Science</subject>
<subj-group>
<subject>Community Case Study</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>RETRACTED: Investigating the Impact of Monetary Progress on Ecological Excellence in Malaysia: Employing Financial Maturity, and Biological Variation</article-title>
<alt-title alt-title-type="left-running-head">Wang et al.</alt-title>
<alt-title alt-title-type="right-running-head">Impact of Monetary Progress on Ecological Excellence</alt-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Wang</surname>
<given-names>Long</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Guo</surname>
<given-names>Jin</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Ahmad</surname>
<given-names>Muneeb</given-names>
</name>
<xref ref-type="aff" rid="aff3">
<sup>3</sup>
</xref>
</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Khan</surname>
<given-names>Yousaf Ali</given-names>
</name>
<xref ref-type="aff" rid="aff4">
<sup>4</sup>
</xref>
<xref ref-type="corresp" rid="c001">&#x2a;</xref>
<xref ref-type="fn" rid="FN1">
<sup>&#x2020;</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/1611364/overview"/>
</contrib>
</contrib-group>
<aff id="aff1">
<label>
<sup>1</sup>
</label>
<institution>School of Hospitality Management</institution>, <institution>Zhejiang Yuexiu University</institution>, <addr-line>Shaoxing</addr-line>, <country>China</country>
</aff>
<aff id="aff2">
<label>
<sup>2</sup>
</label>
<institution>Newcastle Business School</institution>, <institution>Northumbria University</institution>, <addr-line>Newcastle upon Tyne</addr-line>, <country>United Kingdom</country>
</aff>
<aff id="aff3">
<label>
<sup>3</sup>
</label>
<institution>School of Finance</institution>, <institution>Jiangxi University of Finance and Economics</institution>, <addr-line>Nanchang</addr-line>, <country>China</country>
</aff>
<aff id="aff4">
<label>
<sup>4</sup>
</label>
<institution>Department of Mathematics and Statistics</institution>, <institution>Hazara University Mansehra</institution>, <addr-line>Dhodial</addr-line>, <country>Pakistan</country>
</aff>
<author-notes>
<corresp id="c001">&#x2a;Correspondence: Long Wang, <email>1507468286@qq.com</email>; Yousaf Ali Khan, <email>yousaf_hu@yahoo.com</email>
</corresp>
<fn fn-type="other">
<p>This article was submitted to Environmental Economics and Management, a section of the journal Frontiers in Environmental Science</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1104675/overview">Ehsan Elahi</ext-link>, Shandong University of Technology, China</p>
</fn>
<fn fn-type="edited-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1632803/overview">Noman Arshed</ext-link>, University of Education Lahore, Pakistan</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1632890/overview">Mohammad Sharif Karimi</ext-link>, Razi University, Iran</p>
</fn>
<fn fn-type="equal" id="FN1">
<label>
<sup>&#x2020;</sup>
</label>
<p>
<bold>ORCID:</bold> Yousaf Ali Khan orcid.org/<ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="http://0000-0001-9508-7740">0000-0001-9508-7740</ext-link>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>23</day>
<month>02</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="collection">
<year>2022</year>
</pub-date>
<volume>10</volume>
<elocation-id>852379</elocation-id>
<history>
<date date-type="received">
<day>11</day>
<month>01</month>
<year>2022</year>
</date>
<date date-type="accepted">
<day>24</day>
<month>01</month>
<year>2022</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2022 Wang, Guo, Ahmad and Khan.</copyright-statement>
<copyright-year>2022</copyright-year>
<copyright-holder>Wang, Guo, Ahmad and Khan</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>This study investigates the consequences of financial development on environmental quality in Malaysia by using monetary access, profundity, and effectiveness as support components from 1990 to 2019. The level of affiliation between the components explored by using appropriate autoregressive lag technique. The variables appeared to have a long-term relationship during the investigation. Monetary events, population expansion, financial development, and energy consumption contribute to environmental degradation in the short and long-run. In contrast, squared fiscal growth boosts green value in the short and long term. As a result, Malaysia hosts the Ecological Carbon Kuznets Bend (ECKC) with a negative and measurable blunder revision phrase that supports the existence of a level connection between these variables. That is, any prior year&#x2019;s 21.8% imbalance is rectified within a year. Monetary events, financial development, squared monetary development, energy use, and population all impact carbon dioxide flows.</p>
</abstract>
<kwd-group>
<kwd>Malaysian fiscal progress</kwd>
<kwd>CO<sub>2</sub> emissions</kwd>
<kwd>financial escalation</kwd>
<kwd>ecological value</kwd>
<kwd>ecological carbon Kuznets bend</kwd>
</kwd-group>
</article-meta>
</front>
<body>
<sec id="s1">
<title>Introduction</title>
<p>The status of the climate has recently aroused the interest of proponents of a positive turn of events, as it has worsened to dangerous proportions. This financial risk concerns analysts and politicians in both developed and developing countries. Carbon dioxide fluxes from affluent nations have recently gotten much attention. The quest for the factors that cause growing carbon dioxide emissions is never-ending, and research aimed at identifying remedies has produced mixed results. Carbon dioxide emissions are mostly caused by human activities such as creation, use, population, transportation, and urbanization. The status of the economy is inextricably tied to the state of the environment. According to <xref ref-type="bibr" rid="B57">Zhang (2011)</xref>, monetary events affect ecological quality in three ways: first, improved securities exchanges help publicly traded companies lower financing costs, expand financing channels, increase risk, and upgrade resource/risk structures, as well as increase energy utilization and fossil fuel byproducts. Second, higher financial development may attract additional direct investment, boosting economic expansion but simultaneously raising carbon dioxide emissions. Third, practical and professional monetary intermediation facilitates customers&#x2019; advanced activities by making it simpler to acquire expensive things such as luxury houses, climate control systems, and automobiles, refrigerators, and clothes washers. Zhang&#x2019;s carbon footprint will grow as a result (2011). A well-designed account area might also assist in selecting energy-efficient assembly techniques and environmentally friendly buyer items. As a result, the monetary system plays a critical role in emerging and developed economies&#x2019; financial development. Indeed, the board&#x2019;s competent monetary structure assists governments in making the most of their financial resources, even when they have limited financial resources. It creates a more favorable financial environment for progress and supports monetary development described by <xref ref-type="bibr" rid="B19">Furuoka, (2015a)</xref>. A well-thought-out and managed monetary framework attracts investors, improves the securities market, and boosts financial efficiency. Monetary development is a necessary component of every economy since it supports securities market and banking activities. The monetary area is drawn to abnormal direct theory, which helps a country&#x2019;s monetary framework by promoting a financial invention. The monetary system and abnormal direct theory are intimately intertwined (<xref ref-type="bibr" rid="B42">Sadorsky, 2011</xref>). The result of consistent financial success and a healthy financial middle ground generates investment possibilities and expands financing to companies. This program encourages firms and people to start new enterprises and invest in high-value consumables to reduce energy usage. As a result of increased energy usage, carbon dioxide outflows into the environment and environmental contaminants into the world&#x2019;s biological system have risen (<xref ref-type="bibr" rid="B1">Abbasi and Riaz, 2016</xref>; <xref ref-type="bibr" rid="B49">Shahbaz et al., 2016c</xref>; <xref ref-type="bibr" rid="B10">Bekhet et al., 2017</xref>). According to <xref ref-type="bibr" rid="B15">Dasgupta et al. (2004)</xref>, established financial institutions and capital business sectors give chances for investable cash in the sustainable power industry and advance and value finance for environmentally friendly green power projects. The allotment of low-cost credits for ecologically friendly driving is taken into consideration in a well-designed monetary system. FDI may also encourage local firms to develop specialized skills, which can help cut energy use (<xref ref-type="bibr" rid="B13">Claessens and Feijen, 2007</xref>; <xref ref-type="bibr" rid="B54">Tamazian et al., 2009</xref>; <xref ref-type="bibr" rid="B24">Jalil and Feridun, 2011</xref>). <xref ref-type="fig" rid="F1">Figure 1</xref> show how achieving financial success may be utilized to boost energy replacement. In the best-case scenario, the results or consequences of research on the monetary improvement environmental quality nexus remain uncertain. This research aims to look into the relationship between fossil fuel waste and monetary development in Malaysia, which is a rapidly expanding country. The investigation spans the years 1990 through 2019. The autoregressive distributive slack approach, based on <xref ref-type="bibr" rid="B6">Ang&#x2019;s (2007)</xref> CO<sub>2</sub> model and updated with monetary turns of events, is used to construct long-run co-incorporation linkages between CO<sub>2</sub> emissions, energy consumption, population, monetary development, and monetary improvement.</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>CUSUM coefficient stability graph.</p>
</caption>
<graphic xlink:href="fenvs-10-852379-g001.tif"/>
</fig>
<p>The main contribution of this research to the existing literature is as follows:<list list-type="simple">
<list-item>
<p>1. It will conduct its research using a larger collection of financial development lists.</p>
</list-item>
<list-item>
<p>2. It provides policymakers with a solid and econometrically sound result to assist Malaysian officials in developing environmental quality criteria.</p>
</list-item>
</list>
</p>
<p>The remainder of the paper is formatted as follows: <italic>Literature Review</italic> provides a brief overview of the writing, <italic>Methods and Materials</italic> contains an itemized description of the technique, <italic>Results and Discussions</italic> contains a thorough discussion of the econometric findings from our study, and <italic>Conclusions and Recommendations</italic> ends our research with strategic suggestions.</p>
</sec>
<sec id="s2">
<title>Literature Review</title>
<p>The link between monetary fluctuations and fossil fuel byproducts has been studied in a few researches with conflicting findings. For example, <xref ref-type="bibr" rid="B5">Ali et al. (2019)</xref> employed the ARDL bound test approach to investigate the numerous connections between fossil fuel byproducts and monetary development in Malaysia between 1971 and 2010. They observed that monetary improvement has a long-term and short-term impact on fossil fuel byproducts. <xref ref-type="bibr" rid="B36">Mesagan and Nwachukwu (2018)</xref> utilized the ARDL limits testing approach to explore the reasons for an ecological breakdown from 1981 to 2016. By analyzing head components, they create an ecological deterioration file (PCA). They discovered that whereas monetary events, energy consumption, exchange, and play are all important predictors of environmental quality, speculation and urbanization are not.</p>
<p>Similarly, despite unidirectional causation from urbanization pay to ecological suffering. With the bidirectional causality between energy usage and environmental deterioration, no causal relationship has been shown between capital speculation, monetary events, and environmental quality. <xref ref-type="bibr" rid="B40">Rafindadi (2016)</xref> investigates the relationship between monetary events, financial growth, energy consumption, exchange receptiveness, and CO<sub>2</sub> emissions in Malaysia. He discovered a causal relationship between monetary events and energy use and monetary events and CO<sub>2</sub> emissions in both directions. At the input level, there was a connection between monetary development and CO<sub>2</sub> emissions. Islam et al. confirmed both long- and short-run links between Malaysia&#x2019;s monetary events and CO<sub>2</sub> outflows (2013). According to <xref ref-type="bibr" rid="B12">Boutabba (2014)</xref>, the monetary turn of events influences CO<sub>2</sub> fluxes in India. <xref ref-type="bibr" rid="B25">Jiang and Ma (2019)</xref> used a framework summarized next technique to investigate the link between monetary events and fossil fuel byproducts for 155 countries, taking into account the heterogeneity of the sample by isolating the sample into two sub-groupings: developed and developing economies and non-industrial nations.</p>
<p>At the global, emerging business sector, and agricultural nation levels, the statistics reveal that monetary patterns considerably increase fossil fuel byproducts. The impact on industrialized countries, on the other hand, is insignificant. <xref ref-type="bibr" rid="B1">Abbasi and Riaz (2016)</xref> discovered that monetary development is the most important driver of CO<sub>2</sub> emissions in small and developing economies in a comparative study. All types of monetary development, including bank-issued private-sector homegrown credit, bank-issued private-sector homegrown credit, and monetary-issued private-sector homegrown credit, improve environmental quality through reducing environmental effect (<xref ref-type="bibr" rid="B35">Majeed and Mazhar, 2019</xref>). Foreign direct investment, energy use, and GDP per capita all negatively affect environmental quality, whereas domestic lending to the private sector has a greater impact than other monetary growth and urbanization metrics. Economic expansion harms environmental quality in Pakistan, according to <xref ref-type="bibr" rid="B49">Shahbaz et al. (2016c)</xref>. <xref ref-type="bibr" rid="B45">Saleem et al. (2019)</xref> used a different OLS to investigate the effects of GDP growth, discovering that lower-pay economies do not support the EKC hypothesis, but higher-reimbursement economies do. Affirmative (2017) built a board to examine the impact of financial development on CO<sub>2</sub> emissions using a novel board edge method and data from 31 agricultural nations. According to experts, financial growth hurts CO<sub>2</sub> emissions in low-development countries and has a beneficial impact on high-development countries. Their findings do not support the estimated Kuznets curve (EKC) theory. Although they discovered a U-shaped connection, CO<sub>2</sub> emissions were also impacted by energy use and population. <xref ref-type="bibr" rid="B10">Bekhet et al. (2017)</xref> represented that the rise of fossil fuel byproducts in GCC countries other than the UAE is related to monetary events. In the economies of the South Mediterranean, <xref ref-type="bibr" rid="B28">Kahouli (2017)</xref> discovered a one-way causal connection between monetary events and CO<sub>2</sub> emissions (SMEs) as well as money development reduces CO<sub>2</sub> emissions in Pakistan and Bangladesh while increasing CO<sub>2</sub> outflows in India (<xref ref-type="bibr" rid="B30">Khan A. Q. et al., 2017</xref>). A globally developed monetary area may also lower acquisition prices, increase interest in the sustainable power business, and reduce CO<sub>2</sub> emissions. <xref ref-type="bibr" rid="B56">Yuxiang and Chen (2010)</xref> discovered that monetary improvement systems stimulate development, reduce CO<sub>2</sub> emissions, and boost local manufacturing, as <xref ref-type="bibr" rid="B48">Shahbaz et al. (2010)</xref> indicated. <xref ref-type="bibr" rid="B2">Al-mulali and Sab (2012)</xref> showed that a rise in popular interest in monetary events and energy usage increases interest in monetary administrations. It might lead to the financial sector implementing substantial monetary arrangements to reduce CO<sub>2</sub> emissions, and economic progress would lead to financial instability, which would result in increased CO<sub>2</sub> emissions (<xref ref-type="bibr" rid="B57">Zhang, 2011</xref>). According to <xref ref-type="bibr" rid="B20">Haseeb et al. (2018)</xref>, financial measures are the most important boosters of <xref ref-type="bibr" rid="B57">Zhang (2011)</xref>; <xref ref-type="bibr" rid="B50">Siddique (2017)</xref> depletion, and the EKC hypothesis holds across BRICS nations, and higher energy usage and monetary growth resulted in increased carbon dioxide emissions. Energy use and economic development help manufacturing and economic expansion, but carbon dioxide emissions rise.</p>
<p>In any case, some research has shown that monetary reform can aid in the reduction of CO<sub>2</sub> emissions, as <xref ref-type="bibr" rid="B31">Khan M. T. I. et al. (2017)</xref> discovered a bidirectional causal relationship between monetary events and CO<sub>2</sub> emissions in Asia. <xref ref-type="bibr" rid="B41">Riti et al. (2017)</xref> emphasized the importance of monetary improvement in decreasing CO<sub>2</sub> emissions in 90 countries and money events, distributing risk, and enhancing speculation. <xref ref-type="bibr" rid="B53">Tamazian and Rao (2010)</xref> used the GMM method to investigate the impact of institutional, monetary, and monetary changes on CO<sub>2</sub> emissions in transitory economies, and these characteristics assist in reducing CO<sub>2</sub> emissions. (<xref ref-type="bibr" rid="B23">Jalil and Feridun, 2010</xref>) discovered that monetary development in China reduces CO<sub>2</sub> emissions. <xref ref-type="bibr" rid="B34">Lee and Chen (2015)</xref> revealed that monetary development could assist EU countries in reducing CO<sub>2</sub> emissions; EKC does not exist in the EU. According to <xref ref-type="bibr" rid="B55">Xing et al. (2017)</xref>, money growth can help China&#x2019;s fossil fuel byproducts problem, and this impact is not limited to topographical differences. The relationship between monetary events and CO<sub>2</sub> emissions in 12 MENA countries have minimal impact on CO<sub>2</sub> outflows, and economic uncertainty has no influence on CO<sub>2</sub> emissions in Saudi Arabia (<xref ref-type="bibr" rid="B44">Salahuddin et al., 2017</xref>; <xref ref-type="bibr" rid="B37">Omri et al., 2015</xref>; <xref ref-type="bibr" rid="B9">Baloch et al., 2018</xref>).</p>
</sec>
<sec sec-type="materials|methods" id="s3">
<title>Methods and Materials</title>
<p>This study is based on <xref ref-type="bibr" rid="B6">Ang&#x2019;s (2007)</xref> CO<sub>2</sub> model, which has been integrated with financial developments to create CO<sub>2</sub> emissions, energy consumption, population, GDP, and financial development co-integration linkages. <xref ref-type="bibr" rid="B38">Pesaran et al. (2001)</xref> created the autoregressive distributed lag method.</p>
<sec id="s3-1">
<title>Advantages of ARDL Over Other Co-Integration Methods</title>
<p>Endogeneity issues are handled, and the onerous long-run parameter testing associated with the Engle-Granger technique is removed, thanks to the use of suitable delays. Both the long and short-run parameters computed at the same time. Determining the order of integration among variables and unit root testing is lifted from the econometric technique. In reality, whereas the variables in a time series regression equation must be integrated of order one or at least of the same order to be integrated. The variables be <italic>I(1),</italic> only <italic>ARDL</italic> or bound co-integration could be estimated regardless of whether the underlying variables were <italic>I(0), I(1),</italic> or fractionally integrated, and fifth lags length symmetry for the variables is not required. We have applied the following models and techniques to analyze the data of this research (<xref ref-type="bibr" rid="B38">Pesaran et al., 2001</xref>; <xref ref-type="bibr" rid="B5">Ali et al., 2019</xref>).</p>
<p>1. Autoregressive distributive lags model</p>
<p>2. Co-integration test</p>
<p>3. Boundary testing</p>
</sec>
<sec id="s3-2">
<title>Data and Computational Environment</title>
<p>The study looks at the influence of financial growth on Malaysia&#x2019;s ecological excellence and the impact of renewable energy consumption, population expansion, and financial escalation on green value. The needed variables were sourced from the &#x201c;<ext-link ext-link-type="uri" xlink:href="https://www.cgdev.org.com">https://www.cgdev.org.com</ext-link>&#x201d; World Bank&#x2019;s &#x201c;<ext-link ext-link-type="uri" xlink:href="https://www.worldbank.org.com/">https://www.worldbank.org.com</ext-link>&#x201d; recently released global growth map. Financial development, renewable energy consumption, population growth/year, GDP per capita, and carbon dioxide emissions are all factors to consider. From 1990 through 2019, data is gathered by intensity, access, and efficiency as a proxy for the country&#x2019;s financial progress. Furthermore, all of the data in this study was generated using the PLM package and R, a user-friendly statistical analysis program.<xref ref-type="fn" rid="fn1">
<sup>1</sup>
</xref>
</p>
</sec>
</sec>
<sec sec-type="results|discussion" id="s4">
<title>Results and Discussions</title>
<p>As indicated in <xref ref-type="table" rid="T1">Table 1</xref>, the data analysis was performed using outline measurements; energy use has the greatest methods, while GDP has the lowest mean worth of the various parts. On the other hand, energy consumption has the highest least and greatest characteristics, whereas total national production has the lowest and largest. In any case, the standard deviation, which quantifies a variable&#x2019;s fluctuation, shows that carbon dioxide emissions were the most unpredictable of the many components, while monetary improvement was the least consistent. Furthermore, whereas total national output and energy use was skewed, carbon dioxide emissions, monetary events, and population were all skewed in the other direction. Similarly, all characteristics were consistently conveyed since the Jarque-Bera test&#x2019;s probability advantages for essential features at a 5% degree of importance were more than 0.05. The stationarity of the factors was then verified using the unit root test. <xref ref-type="table" rid="T2">Table 2</xref> shows the results of the unit root test. Except for population, none of the variables was set at the level, although they were distinct.</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>Stationarity of factors in summary statistics.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Variables</th>
<th align="center">Mean</th>
<th align="center">Std. dev</th>
<th align="center">Max</th>
<th align="center">Mini</th>
<th align="center">Skew</th>
<th align="center">Kurt</th>
<th align="center">Jar-B</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">In_CO<sub>2</sub>
</td>
<td align="char" char=".">12.10</td>
<td align="char" char=".">0.46</td>
<td align="char" char=".">12.32</td>
<td align="char" char=".">11.22</td>
<td align="char" char=".">&#x2212;0.42</td>
<td align="char" char=".">2.26</td>
<td align="char" char="(">4.04 (0.001)</td>
</tr>
<tr>
<td align="left">In_FD</td>
<td align="char" char=".">3.23</td>
<td align="char" char=".">0.05</td>
<td align="char" char=".">3.56</td>
<td align="char" char=".">3.19</td>
<td align="char" char=".">&#x2212;0.53</td>
<td align="char" char=".">3.45</td>
<td align="char" char="(">0.42 (0.000)</td>
</tr>
<tr>
<td align="left">In_GDP</td>
<td align="char" char=".">3.21</td>
<td align="char" char=".">0.32</td>
<td align="char" char=".">3.49</td>
<td align="char" char=".">3.43</td>
<td align="char" char=".">0.82</td>
<td align="char" char=".">3.21</td>
<td align="char" char="(">4.25 (0.003)</td>
</tr>
<tr>
<td align="left">In_POP</td>
<td align="char" char=".">7.99</td>
<td align="char" char=".">2.43</td>
<td align="char" char=".">12.41</td>
<td align="char" char=".">5.29</td>
<td align="char" char=".">&#x2212;0.09</td>
<td align="char" char=".">2.08</td>
<td align="char" char="(">3.79 (0.000)</td>
</tr>
<tr>
<td align="left">In_ENG</td>
<td align="char" char=".">19.62</td>
<td align="char" char=".">0.37</td>
<td align="char" char=".">19.20</td>
<td align="char" char=".">19.52</td>
<td align="char" char=".">0.03</td>
<td align="char" char=".">2.06</td>
<td align="char" char="(">2.12 (0.001)</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>Unit root test and critical value results for ADF.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th colspan="7" align="center">Variables</th>
</tr>
<tr>
<th align="left"/>
<th align="left"/>
<th align="center">In_CO<sub>2</sub>
</th>
<th align="center">In_FID</th>
<th align="center">In_GDP</th>
<th align="center">In_POP</th>
<th align="center">In_ENG</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td rowspan="3" align="left">Level</td>
<td rowspan="2" align="left">ADF</td>
<td align="center">&#x2212;1.7946</td>
<td align="center">&#x2212;2.1391</td>
<td align="center">&#x2212;1.4287</td>
<td align="center">&#x2212;5.6923</td>
<td align="center">&#x2212;2.6569</td>
</tr>
<tr>
<td align="center">&#x2212;0.6844</td>
<td align="center">&#x2212;0.5067</td>
<td align="center">&#x2212;0.8334</td>
<td align="center">&#x2212;0.0005</td>
<td align="center">&#x2212;0.0921</td>
</tr>
<tr>
<td align="left">Critical value</td>
<td align="center">&#x2212;3.5539</td>
<td align="center">&#x2212;3.5539</td>
<td align="center">&#x2212;3.5539</td>
<td align="center">&#x2212;3.5539</td>
<td align="center">&#x2212;3.5539</td>
</tr>
<tr>
<td align="left"/>
<td align="left">Result</td>
<td align="center">
<italic>I(1)</italic>
</td>
<td align="center">
<italic>I(1)</italic>
</td>
<td align="center">
<italic>I(1)</italic>
</td>
<td align="center">
<italic>I(1)</italic>
</td>
<td align="center">
<italic>I(1)</italic>
</td>
</tr>
<tr>
<td rowspan="4" align="left">First difference</td>
<td rowspan="2" align="left">ADF</td>
<td align="center">&#x2212;5.5568</td>
<td align="center">5.2361</td>
<td align="center">&#x2212;3.1951</td>
<td rowspan="2" align="center">&#x2212;&#x2014;</td>
<td align="center">&#x2212;6.0845</td>
</tr>
<tr>
<td align="center">&#x2212;0.0001</td>
<td align="center">&#x2212;0.0002</td>
<td align="center">&#x2212;0.0296</td>
<td align="center">0</td>
</tr>
<tr>
<td align="left">Significant value</td>
<td align="center">&#x2212;3.2456</td>
<td align="center">&#x2212;3.2456</td>
<td align="center">&#x2212;3.2456</td>
<td align="center">&#x2212;3.2456</td>
<td align="center">&#x2212;3.2456</td>
</tr>
<tr>
<td align="left">Result</td>
<td align="center">
<italic>I(0)</italic>
</td>
<td align="center">
<italic>I(0)</italic>
</td>
<td align="center">
<italic>I(0)</italic>
</td>
<td align="center">
<italic>I(0)</italic>
</td>
<td align="center">
<italic>I(0)</italic>
</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Values in parentheses are MacKinnon (1996) one-sided <italic>p</italic>-values. Critical values are obtained from the Augmented Dickey-Fuller test result.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>All the I(1) elements were absorbed as one, except the population, which absorbed as zero, such as I(0). The most noticeable request was one because the elements comprised various requests. Using the ARDL technique, we continue our co-reconciliation research.</p>
<sec id="s4-1">
<title>Appraisals of the ARDL Model&#x2019;s Lag Duration</title>
<p>The first step in evaluating an ARDL model is determining the length of the optimal slack for each element in the model. Using the standard un-constrained VAR method for all variables, as with other classes of autoregressive models, does not produce good results for the ARDL model. The Akaike information curve (AIC), Schwarz information curve (SIC), Hannan-Quinn curve (HQC), and Adjusted R-squared all include slack components for solitary factors that are quite similar (AR). <xref ref-type="table" rid="T3">Table 3</xref> shows the results of our model&#x2019;s slack lengths purpose source using four data measures.</p>
<table-wrap id="T3" position="float">
<label>TABLE 3</label>
<caption>
<p>Model selection criteria.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left" rowspan="3">Information criteria</th>
<th colspan="6" align="center">ARDL model&#x2019;s lag period results</th>
</tr>
<tr>
<th colspan="6" align="center">ARDL (3, 0, 2, 3, 2, 2)</th>
</tr>
<tr>
<th align="center">
<inline-formula id="inf1">
<mml:math id="m1">
<mml:mrow>
<mml:msubsup>
<mml:mi>X</mml:mi>
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mi>F</mml:mi>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula>
</th>
<th align="center">
<inline-formula id="inf2">
<mml:math id="m2">
<mml:mrow>
<mml:msubsup>
<mml:mi>X</mml:mi>
<mml:mi>H</mml:mi>
<mml:mn>2</mml:mn>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula>
</th>
<th align="center">
<inline-formula id="inf3">
<mml:math id="m3">
<mml:mrow>
<mml:msubsup>
<mml:mi>X</mml:mi>
<mml:mrow>
<mml:mi>A</mml:mi>
<mml:mi>R</mml:mi>
<mml:mi>C</mml:mi>
<mml:mi>H</mml:mi>
<mml:mrow>
<mml:mo>(</mml:mo>
<mml:mn>1</mml:mn>
<mml:mo>)</mml:mo>
</mml:mrow>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula>
</th>
<th align="center">
<inline-formula id="inf4">
<mml:math id="m4">
<mml:mrow>
<mml:msubsup>
<mml:mi>X</mml:mi>
<mml:mrow>
<mml:mi>S</mml:mi>
<mml:mi>C</mml:mi>
<mml:mrow>
<mml:mo>(</mml:mo>
<mml:mn>1</mml:mn>
<mml:mo>)</mml:mo>
</mml:mrow>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula>
</th>
<th align="center">
<inline-formula id="inf5">
<mml:math id="m5">
<mml:mrow>
<mml:msubsup>
<mml:mi>X</mml:mi>
<mml:mrow>
<mml:mi>S</mml:mi>
<mml:mi>C</mml:mi>
<mml:mrow>
<mml:mo>(</mml:mo>
<mml:mn>2</mml:mn>
<mml:mo>)</mml:mo>
</mml:mrow>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula>
</th>
<th align="center">
<inline-formula id="inf6">
<mml:math id="m6">
<mml:mrow>
<mml:msubsup>
<mml:mi>X</mml:mi>
<mml:mi>N</mml:mi>
<mml:mn>2</mml:mn>
</mml:msubsup>
</mml:mrow>
</mml:math>
</inline-formula>
</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Akaike information criteria (AIC)</td>
<td align="char" char=".">0.1015</td>
<td align="char" char=".">0.8375</td>
<td align="char" char=".">0.3563</td>
<td align="char" char=".">0.7691</td>
<td align="char" char=".">0.2984</td>
<td align="char" char=".">0.8765</td>
</tr>
<tr>
<td align="left">Schwarz information criteria (SIC)</td>
<td align="char" char=".">0.1015</td>
<td align="char" char=".">0.8375</td>
<td align="char" char=".">0.3563</td>
<td align="char" char=".">0.6235</td>
<td align="char" char=".">0.4773</td>
<td align="char" char=".">0.8765</td>
</tr>
<tr>
<td align="left">Hannan-Quinn criteria (HQC)</td>
<td align="char" char=".">0.1015</td>
<td align="char" char=".">0.8375</td>
<td align="char" char=".">0.3563</td>
<td align="char" char=".">0.5733</td>
<td align="char" char=".">0.2984</td>
<td align="char" char=".">0.8765</td>
</tr>
<tr>
<td align="left">Adjusted R-squared (AR)</td>
<td align="char" char=".">0.2109</td>
<td align="char" char=".">0.9239</td>
<td align="char" char=".">0.3840</td>
<td align="char" char=".">0.0539</td>
<td align="char" char=".">0.3098</td>
<td align="char" char=".">0.9235</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>
<inline-formula id="inf7">
<mml:math id="m7">
<mml:mrow>
<mml:msubsup>
<mml:mi>&#x3c7;</mml:mi>
<mml:mrow>
<mml:mi>F</mml:mi>
<mml:mi>F</mml:mi>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msubsup>
<mml:mo>,</mml:mo>
<mml:mtext>&#x2002;</mml:mtext>
<mml:msubsup>
<mml:mi>&#x3c7;</mml:mi>
<mml:mi>H</mml:mi>
<mml:mn>2</mml:mn>
</mml:msubsup>
<mml:mo>,</mml:mo>
<mml:mtext>&#x2002;</mml:mtext>
<mml:msubsup>
<mml:mi>&#x3c7;</mml:mi>
<mml:mrow>
<mml:mi>A</mml:mi>
<mml:mi>R</mml:mi>
<mml:mi>C</mml:mi>
<mml:mi>H</mml:mi>
<mml:mrow>
<mml:mo>(</mml:mo>
<mml:mn>1</mml:mn>
<mml:mo>)</mml:mo>
</mml:mrow>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msubsup>
<mml:mo>,</mml:mo>
<mml:mtext>&#x2002;</mml:mtext>
<mml:mo>&#xa0;</mml:mo>
<mml:msubsup>
<mml:mi>&#x3c7;</mml:mi>
<mml:mrow>
<mml:mi>S</mml:mi>
<mml:mi>C</mml:mi>
<mml:mrow>
<mml:mo>(</mml:mo>
<mml:mn>1</mml:mn>
<mml:mo>)</mml:mo>
</mml:mrow>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msubsup>
<mml:mo>,</mml:mo>
<mml:mtext>&#x2002;</mml:mtext>
<mml:mo>&#xa0;</mml:mo>
<mml:msubsup>
<mml:mi>&#x3c7;</mml:mi>
<mml:mrow>
<mml:mi>S</mml:mi>
<mml:mi>C</mml:mi>
<mml:mrow>
<mml:mo>(</mml:mo>
<mml:mn>2</mml:mn>
<mml:mo>)</mml:mo>
</mml:mrow>
</mml:mrow>
<mml:mn>2</mml:mn>
</mml:msubsup>
<mml:mo>,</mml:mo>
<mml:mtext>&#x2002;</mml:mtext>
<mml:msubsup>
<mml:mi>&#x3c7;</mml:mi>
<mml:mi>N</mml:mi>
<mml:mn>2</mml:mn>
</mml:msubsup>
<mml:mo>&#xa0;</mml:mo>
</mml:mrow>
</mml:math>
</inline-formula> are functional form misspecification, residual heteroscedasticity. The ARCH effect, autocorrelation, and non-normal error are all tested using the Lagrange multiplier statistic.</p>
</fn>
</table-wrap-foot>
</table-wrap>
<p>The AR demonstrative test rejects one of OLS&#x2019;s suppositions. The ARDL (3, 0, 2, 3, 2, 2) model is then used to examine co-joining between these components, revealing that the erroneous component has requested autocorrelation first. Furthermore, the symptomatic test for these data rules shows that these models are appropriately indices. Consequently, as <xref ref-type="bibr" rid="B38">Pesaran et al. (2001)</xref> point out, the selected model is relevant for consideration; sequential connection influences co-reconciliation results, requiring the use of analytic tests before assessing co-combination for the chosen model.</p>
</sec>
<sec id="s4-2">
<title>The Autoregressive Distributed Lag Bound Test</title>
<p>Following the identification of eligible deferrals, the ARDL bound co-joining test assessment is the next step. The exam was completed using Eqn. 14 and the Equations method to standardize, utilizing chosen slacks (3, 0, 2, 3, 2, 2) and assessing an autoregressive circulating slack model in <xref ref-type="table" rid="T4">Table 4</xref>, as proposed by <xref ref-type="bibr" rid="B39">Pesaran and Shin (1999)</xref>. Adjusted R-squared (AR) revealed that the incorrect component had requested autocorrelation first. The AR demonstrative test ignores one of OLS&#x2019;s suppositions, and the ARDL (3, 0, 2, 3, 2, 2) model is then used to assess co-joining between these components. Furthermore, the symptomatic test for these data rules demonstrates that these models are correctly indicated, that there is no first or second request autocorrelation. The heteroscedasticity and curve do not impact the lingering. They are properly applied on a regular source. As a result, the chosen model is appropriate for consideration as <xref ref-type="bibr" rid="B38">Pesaran et al. (2001)</xref> point out; the sequential connection impacts co-reconciliation outcomes, involving the employ of analytic tests before evaluating co-combination for the chosen model.</p>
<table-wrap id="T4" position="float">
<label>TABLE 4</label>
<caption>
<p>Bound co-integration test.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Depended variables</th>
<th align="center">No. variables</th>
<th align="center">F-statistics</th>
<th align="center">Decision</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">F<sub>CO2</sub>(CO<sub>2</sub>&#x1c0; GDP, GDP<sup>2</sup>, FD, POP, ENG)</td>
<td align="center">5</td>
<td align="center">5.76</td>
<td align="left">Co-integration</td>
</tr>
<tr>
<td align="left">F<sub>GDP</sub>(GDP&#x1c0; CO<sub>2</sub>, GDP<sup>2</sup>,FD, POP, ENG)</td>
<td align="center">5</td>
<td align="center">46.81</td>
<td align="left">Co-integration</td>
</tr>
<tr>
<td align="left">F<sub>GDP</sub>
<sup>2</sup>(GDP<sup>2</sup>&#x1c0; GDP, CO<sub>2</sub>,FD, POP, ENG)</td>
<td align="center">5</td>
<td align="center">18.46</td>
<td align="left">Co-integration</td>
</tr>
<tr>
<td align="left">F<sub>FD</sub>(FD&#x1c0; GDP<sup>2</sup>, GDP,CO2, POP, ENG)</td>
<td align="center">5</td>
<td align="center">6.59</td>
<td align="left">Co-integration</td>
</tr>
<tr>
<td align="left">F<sub>POP</sub>(POP&#x1c0; FD, GDP2,GDP,CO<sub>2</sub>, ENG)</td>
<td align="center">5</td>
<td align="center">77.3</td>
<td align="left">Co-integration</td>
</tr>
<tr>
<td align="left">F<sub>ENG</sub>(ENG&#x1c0; POP, FD,GDP2,GDP,CO<sub>2</sub>)</td>
<td align="center">5</td>
<td align="center">5.43</td>
<td align="left">Co-integration</td>
</tr>
<tr>
<td align="left">
<xref ref-type="bibr" rid="B38">Pesaran et al. (2001)</xref> critical value</td>
<td align="center">10%</td>
<td align="center">5%</td>
<td align="left">1%</td>
</tr>
<tr>
<td align="left">I(0) bound</td>
<td align="center">2.27</td>
<td align="center">2.63</td>
<td align="left">3.42</td>
</tr>
<tr>
<td align="left">I(1) bound</td>
<td align="center">3.35</td>
<td align="center">3.79</td>
<td align="left">4.69</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>Setting a significant slack level constraint for all components using F-measurement and the Wald test to build up the joint meaning of the level factors is required for this test (bound test). CO<sub>2</sub> emissions, monetary events, total national output (GDP), squared GDP, population (POP), and energy use (ENG) are all demonstrated to be perfectly synchronized in <xref ref-type="table" rid="T4">Table 4</xref>. According to the F-measurement from the bound test, the upper bound of 3.79% Pesaran basic worth 5% huge level, five autonomous components (k &#x3d; 5) with no steadiness and pattern, case 1, was higher than the maximum furthest reaches of 3.79% Pesaran basic worth 5% important level. As a result, these variables have maintained a long-standing run connection, implying that they move in lockstep throughout time. Because the parts are so interwoven, a blunder correction model is needed to forecast the rate of change in the short term, when they could drift apart.</p>
<p>
<xref ref-type="table" rid="T5">Table 5</xref> shows the short-run model&#x2019;s assessed result after selecting whether or not the elements are co-incorporated, whereas <xref ref-type="table" rid="T6">Table 6</xref> shows the long-run model. The country&#x2019;s economic progress influences the quality of Malaysia&#x2019;s environmental environment. According to this model, a 1% increase in monetary advancement in Malaysia would result in 0.78 and 1.33% reductions in environmental quality, which is substantial in both the short and long term. The ARDL method and increasing monetary value-enhanced environmental quality and a rise in population will increase carbon dioxide fluxes shortly. The underlying population slack causes a reduction in fossil fuel byproducts in the short run. In Malaysia, for example, a 1% increase in population would result in a 316.5% rise in CO<sub>2</sub> emissions shortly, but a 1% increase in first slacks would result in a 421.4% decrease in CO<sub>2</sub>. Population growth has a substantial influence on environmental quality over time; a 1% increase in population translates into a 4.85% decrease in ecological quality. Furthermore, GDP impacts the environment; a 1% rise in GDP leads to 1.19 and 1.59% increases in environmental catastrophes in the medium and long term, respectively. On the other side, the squared total national production influences environmental quality. A 1% rise in squared GDP results in a 0.13 and 0.08% deterioration in environmental quality in the short and long run, respectively. Since it observed that squared Gross Domestic Product impacted CO<sub>2</sub> emissions, it reasoned that the Environmental Carbon Kuznets Bend (ECKC) existed in Malaysia. Finally, energy use impacts ecological quality; a 1% increase in energy use results in a 1.61 and 1.09% reduction in environmental quality over the medium and long term, respectively. Increased energy usage, it was believed, had a major short- and long-term influence on environmental collapse. Overall, verified fossil fuel byproducts have a substantial impact on public opinion of fossil fuel byproducts. Finally, a 1% increase in historical CO<sub>2</sub> fluxes translates into a 1.33% rise in Malaysia&#x2019;s present environmental disintegration. The error correction term ECT (&#x2212;1) is negative and extremely large, confirming the newly established co-mix relationship between the components. Any prior year&#x2019;s disequilibrium is redressed by 21.8% each year, while the rest of the variables remain constant. Furthermore, autonomous components independently explain 60 and 83% of the short and long-run variation in the dependent variable in <xref ref-type="table" rid="T5">Tables 5</xref>, <xref ref-type="table" rid="T6">6</xref>, respectively. The autonomous features are a significant indicator of the dependent variable in the short and long term, according to F-measurements 4.23 and 26.83.</p>
<table-wrap id="T5" position="float">
<label>TABLE 5</label>
<caption>
<p>Parsimonious estimation for short-run model.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Variables</th>
<th align="center">Coefficient</th>
<th align="center">Standard error</th>
<th align="center">t-statistics</th>
<th align="center">
<italic>p</italic>-values</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Constant</td>
<td align="char" char=".">3.05</td>
<td align="char" char=".">1.02</td>
<td align="char" char=".">2.98</td>
<td align="char" char=".">0.008</td>
</tr>
<tr>
<td align="left">D(LCO2(&#x2212;1))</td>
<td align="char" char=".">1.33</td>
<td align="char" char=".">0.34</td>
<td align="char" char=".">3.89</td>
<td align="char" char=".">0.001</td>
</tr>
<tr>
<td align="left">D(LCO2(&#x2212;2))</td>
<td align="char" char=".">0.19</td>
<td align="char" char=".">0.17</td>
<td align="char" char=".">1.17</td>
<td align="char" char=".">0.262</td>
</tr>
<tr>
<td align="left">D(LGDP)</td>
<td align="char" char=".">1.19</td>
<td align="char" char=".">0.75</td>
<td align="char" char=".">1.59</td>
<td align="char" char=".">0.132</td>
</tr>
<tr>
<td align="left">D(LGDP<sup>2</sup>)</td>
<td align="char" char=".">&#x2212;0.13</td>
<td align="char" char=".">0.05</td>
<td align="char" char=".">&#x2212;2.74</td>
<td align="char" char=".">0.014</td>
</tr>
<tr>
<td align="left">D(LGDP<sup>2</sup>(&#x2212;1))</td>
<td align="char" char=".">&#x2212;0.05</td>
<td align="char" char=".">0.03</td>
<td align="char" char=".">&#x2212;2.08</td>
<td align="char" char=".">0.053</td>
</tr>
<tr>
<td align="left">D(LGDP<sup>2</sup>(-2))</td>
<td align="char" char=".">&#x2212;0.05</td>
<td align="char" char=".">0.02</td>
<td align="char" char=".">&#x2212;2.44</td>
<td align="char" char=".">0.026</td>
</tr>
<tr>
<td align="left">D(LFD)</td>
<td align="char" char=".">0.78</td>
<td align="char" char=".">0.28</td>
<td align="char" char=".">2.73</td>
<td align="char" char=".">0.015</td>
</tr>
<tr>
<td align="left">D(LFD(&#x2212;1))</td>
<td align="char" char=".">&#x2212;0.59</td>
<td align="char" char=".">0.34</td>
<td align="char" char=".">&#x2212;1.73</td>
<td align="char" char=".">0.105</td>
</tr>
<tr>
<td align="left">D(LFD(&#x2212;2))</td>
<td align="char" char=".">&#x2212;0.43</td>
<td align="char" char=".">0.29</td>
<td align="char" char=".">&#x2212;1.45</td>
<td align="char" char=".">0.171</td>
</tr>
<tr>
<td align="left">D(LENG)</td>
<td align="char" char=".">1.61</td>
<td align="char" char=".">0.36</td>
<td align="char" char=".">4.43</td>
<td align="char" char=".">0</td>
</tr>
<tr>
<td align="left">D(LENG(&#x2212;1))</td>
<td align="char" char=".">&#x2212;0.61</td>
<td align="char" char=".">0.47</td>
<td align="char" char=".">&#x2212;1.29</td>
<td align="char" char=".">0.219</td>
</tr>
<tr>
<td align="left">D(LPOP)</td>
<td align="char" char=".">316.5</td>
<td align="char" char=".">174.08</td>
<td align="char" char=".">1.84</td>
<td align="char" char=".">0.085</td>
</tr>
<tr>
<td align="left">D(LPOP(&#x2212;1))</td>
<td align="char" char=".">&#x2212;421.44</td>
<td align="char" char=".">161.27</td>
<td align="char" char=".">&#x2212;2.69</td>
<td align="char" char=".">0.016</td>
</tr>
<tr>
<td align="left">ECM(&#x2212;1)</td>
<td align="char" char=".">&#x2212;0.218</td>
<td align="char" char=".">0.084</td>
<td align="char" char=".">&#x2212;2.58</td>
<td align="char" char=".">0.002</td>
</tr>
<tr>
<td align="left">R-square</td>
<td align="char" char=".">0.7864</td>
<td rowspan="5" align="left"/>
<td rowspan="5" align="left"/>
<td rowspan="5" align="left"/>
</tr>
<tr>
<td align="left">Adj.R-square</td>
<td align="char" char=".">0.6036</td>
</tr>
<tr>
<td align="left">F-statistic</td>
<td align="char" char=".">4.2372</td>
</tr>
<tr>
<td align="left"/>
<td align="char" char=".">0</td>
</tr>
<tr>
<td align="left">DW</td>
<td align="char" char=".">2.6081</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T6" position="float">
<label>TABLE 6</label>
<caption>
<p>Parsimonious estimation for long-run model.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Variables</th>
<th align="center">Coefficient</th>
<th align="center">Standard error</th>
<th align="center">t-statistics</th>
<th align="center">
<italic>p</italic>-values</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Constant</td>
<td align="char" char=".">99.94</td>
<td align="char" char=".">54.52</td>
<td align="char" char=".">1.84</td>
<td align="char" char=".">0.0783</td>
</tr>
<tr>
<td align="left">LGDP</td>
<td align="char" char=".">1.52</td>
<td align="char" char=".">0.54</td>
<td align="char" char=".">2.86</td>
<td align="char" char=".">0.0437</td>
</tr>
<tr>
<td align="left">LGDP2</td>
<td align="char" char=".">&#x2212;0.09</td>
<td align="char" char=".">0.04</td>
<td align="char" char=".">&#x2212;2.69</td>
<td align="char" char=".">0.0211</td>
</tr>
<tr>
<td align="left">LFD</td>
<td align="char" char=".">1.38</td>
<td align="char" char=".">0.33</td>
<td align="char" char=".">4.27</td>
<td align="char" char=".">0.0049</td>
</tr>
<tr>
<td align="left">LENG</td>
<td align="char" char=".">1.11</td>
<td align="char" char=".">0.31</td>
<td align="char" char=".">3.31</td>
<td align="char" char=".">0.018</td>
</tr>
<tr>
<td align="left">LPOP</td>
<td align="char" char=".">&#x2212;4.85</td>
<td align="char" char=".">1.14</td>
<td align="char" char=".">4.4</td>
<td align="char" char=".">0.0125</td>
</tr>
<tr>
<td align="left">R-square</td>
<td align="char" char=".">0.87</td>
<td rowspan="5" align="left"/>
<td rowspan="5" align="left"/>
<td rowspan="5" align="left"/>
</tr>
<tr>
<td align="left">Adj. R-square</td>
<td align="char" char=".">0.8378</td>
</tr>
<tr>
<td align="left">F-statistic</td>
<td align="char" char=".">27.84</td>
</tr>
<tr>
<td align="left"/>
<td align="char" char=".">0</td>
</tr>
<tr>
<td align="left">DW</td>
<td align="char" char=".">2.0874</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
<sec id="s4-3">
<title>Diagnostic Testing</title>
<p>The analytic test results for the utilitarian kind of the misspecification test are shown in <xref ref-type="table" rid="T7">Table 7</xref>. The model is free of work structure misspecification bias, and the error difference is homoscedastic. There is no Curve impact in the model, that there is no first and second request autocorrelation in the leftover, and that the residuals are normally dispersed and repetitive sound in both the short and long runs.</p>
<table-wrap id="T7" position="float">
<label>TABLE 7</label>
<caption>
<p>Diagnostic tests result of short and long run.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Lagrange multiplier</th>
<th colspan="2" align="center">Model type</th>
</tr>
<tr>
<th align="left">Test statistics</th>
<th align="center">Short run</th>
<th align="center">Long run</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Non-normal error</td>
<td align="char" char=".">0.6993</td>
<td align="center">1.5742</td>
</tr>
<tr>
<td align="left"/>
<td align="char" char=".">&#x2212;0.77049</td>
<td align="center">&#x2212;0.6951</td>
</tr>
<tr>
<td align="left">Autocorrelation</td>
<td align="char" char=".">2.2256</td>
<td align="center">0.1211</td>
</tr>
<tr>
<td align="left">(serial correlation)</td>
<td align="char" char=".">&#x2212;0.1565</td>
<td align="center">&#x2212;0.7203</td>
</tr>
<tr>
<td align="left">Autocorrelation</td>
<td align="char" char=".">2.7813</td>
<td align="center">1.5651</td>
</tr>
<tr>
<td align="left">(serial correlation)</td>
<td align="char" char=".">&#x2212;0.0961</td>
<td align="center">&#x2212;0.2296</td>
</tr>
<tr>
<td align="left">ARCH effect</td>
<td align="char" char=".">0.6822</td>
<td align="center">0.0651</td>
</tr>
<tr>
<td align="left"/>
<td align="char" char=".">&#x2212;0.70142</td>
<td align="center">&#x2212;0.78002</td>
</tr>
<tr>
<td align="left">Residual heteroscedasticity</td>
<td align="char" char=".">2.1881</td>
<td align="center">0.409</td>
</tr>
<tr>
<td align="left"/>
<td align="char" char=".">&#x2212;0.0676</td>
<td align="center">&#x2212;0.8382</td>
</tr>
<tr>
<td align="left">Functional form specification</td>
<td align="char" char=".">0.312</td>
<td align="center">0.3242</td>
</tr>
<tr>
<td align="left"/>
<td align="char" char=".">&#x2212;0.7593</td>
<td align="center">(0.7485)</td>
</tr>
</tbody>
</table>
</table-wrap>
<p>In addition, the coefficient dependability test revealed that the short and long-run coefficients were equally stable. Outwardly, the results of the strength tests are depicted in <xref ref-type="fig" rid="F1">Figures 1</xref>, <xref ref-type="fig" rid="F2">2</xref>. The green bend depicts the CUSUM squared test bends, while the spotted line depicts the bottom and basic upper bounds of the 5% significance level.</p>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>CUSUM squared coefficient stability graph.</p>
</caption>
<graphic xlink:href="fenvs-10-852379-g002.tif"/>
</fig>
<p>The CUSUM squared bends work within the most minimal and higher bounds of the 5% degree of relevance, respectively, demonstrating the coefficients&#x2019; reliability in the two graphs.</p>
</sec>
<sec id="s4-4">
<title>Short Run Causality Analysis</title>
<p>The test was carried out on the stingy result obtained from the late arrival by establishing joint limits on change and slacks of each component using F-measurement and the Wald test; incorrect theory: the factors are all zero. <xref ref-type="table" rid="T8">Table 8</xref> exhibits short-run causation among components in the carbon dioxide emanation CO<sub>2</sub> model. Soon, monetary events, financial expansion, energy use, and population all contribute to carbon dioxide emissions.</p>
<table-wrap id="T8" position="float">
<label>TABLE 8</label>
<caption>
<p>Short-run causality analysis.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Direction</th>
<th align="center">F-statistics</th>
<th align="center">
<italic>p</italic>-value</th>
<th align="center">Decision</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Financial development &#x2192; CO<sub>2</sub>
</td>
<td align="char" char=".">10.5657</td>
<td align="char" char=".">0.0071</td>
<td align="center">Causality exist</td>
</tr>
<tr>
<td align="left">Economic growth &#x2192; CO<sub>2</sub>
</td>
<td align="char" char=".">7.3931</td>
<td align="char" char=".">0.003</td>
<td align="center">Causality exist</td>
</tr>
<tr>
<td align="left">Population &#x2192; CO<sub>2</sub>
</td>
<td align="char" char=".">8.4721</td>
<td align="char" char=".">0.0006</td>
<td align="center">Causality exist</td>
</tr>
<tr>
<td align="left">Energy use &#x2192; CO<sub>2</sub>
</td>
<td align="char" char=".">14.623</td>
<td align="char" char=".">0.0005</td>
<td align="center">Causality exist</td>
</tr>
<tr>
<td align="left">Squared economic growth &#x2192; CO<sub>2</sub>
</td>
<td align="char" char=".">11.7301</td>
<td align="char" char=".">0.0085</td>
<td align="center">Causality exist</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec id="s5">
<title>Conclusions and Recommendations</title>
<p>The impact of environmental quality on Malaysia&#x2019;s economy&#x2019;s long-term development and improvement and its implications for the economy&#x2019;s long-term suitability could not be more crucial in strategy planning and execution. This research looked at the effects of monetary development on environmental quality in Malaysia from 1990 to 2019. The researchers used the autoregressive circulated slack method to look at the level affiliation between the variables of interest: CO<sub>2</sub>, monetary turn of events, energy consumption, financial development, squared financial development, and population. In Malaysia, monetary events, population growth, financial expansion, and energy use are all major factors contributing to the environmental deterioration in the short and long term. In the short and long term, squared monetary development greatly enhances environmental quality. The ecological carbon Kuznets curve (ECKC) is still in use in Malaysia. The findings also show that this variable did have a level correlation (over time), which is supported by the negative and quantifiable meaning of the blunder revision term. That is, any 21.8% imbalance from the previous year is restored within a year. Carbon dioxide fluxes are influenced by monetary events, financial development, squared monetary development, energy usage, and population.</p>
<sec id="s5-1">
<title>Recommendations</title>
<p>The results include the necessity to allocate monetary assets to sustainable power sources and the adoption of innovative technologies that will reduce fossil fuel byproducts and improve environmental quality. Malaysia&#x2019;s government should take the lead in effectively receiving sustainable power innovation and decommissioning nonrenewable innovation, much as population programs have aimed to limit the country&#x2019;s massive population growth in recent years.</p>
</sec>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s6">
<title>Data Availability Statement</title>
<p>Data used in this research are taken from Center for Global Development available at <ext-link ext-link-type="uri" xlink:href="https://www.cgdev.org.com">https://www.cgdev.org.com</ext-link> and World Bank&#x2019;s available at <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://www.worldbank.org.com">https://www.worldbank.org.com</ext-link>.</p>
</sec>
<sec id="s7">
<title>Author Contributions</title>
<p>Conceplization: JG and YK. Methodology: YK. Analysis: MA and LW Supervision: YK. Resourses: LW and MA Written and edition of original manuscript: YK and MA.</p>
</sec>
<sec id="s8">
<title>Funding</title>
<p>The authors acknowledge the financial support from the Philosophy and Social Science Fund of Tianjin City, China (Project No. TJYJ20-012).</p>
</sec>
<sec sec-type="COI-statement" id="s9">
<title>Conflict of Interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="disclaimer" id="s10">
<title>Publisher&#x2019;s Note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<fn-group>
<fn id="fn1">
<label>1</label>
<p>
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</fn>
</fn-group>
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