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CHAPTER 1: RESEARCH OVERVIEW. RESEARCH BACKGROUND.

Forest area (% of land area) - Malaysia (2000 – 2020).

Unemployment rate – Malaysia (2000 – 2022)​. Chea Zhi Hou 2003863.

CO2 emissions (metric tons per capita) - Malaysia (2000 – 2022)​.

Inflation rate - Malaysia (2000 to 2022)​. Chea Zhi Hou 2003863.

Problem Statement. FDI-related factors, such as supply chain disruptions and rising costs, can contribute to inflationary pressures, affecting consumer purchasing power and economic stability in Malaysia..

RESEARCH OBJECTIVE. To examine whether there is significant relationship between forest area and foreign direct investment in Malaysia..

FDI in Malaysia impacts unemployment by enhancing skills through technology transfer, guiding policymakers to adapt education and training for FDI-driven needs.

Literature Review. A comprehensive analysis and synthesis of existing scholarly literature on a specific topic A critical examination of research findings, theories, and methodologies relevant to the subject Provide context, credibility, and identify research gaps between dependent and independent variables. Time frame used year 2000 to 2020 (20 observations) Method used EViews secondary data Data source (wordlbank data).

Independent variables (Research Gaps). CO2 Emission.

Independent variables (Research Gaps). Forest Area.

3 independent variables show positive relationship to the dependent variable 1 independent variable shows negative relationship to the dependent variable 13 authors have been listed to show the relationships between FDI and the dependents variable.

Year Author Relationship 2018 Rabiul Islam & Ahmad Bashawir Abdul Ghani Negative relationship 2023 Piabuo et al. Negative relationship 2022 Raihan and Tuspekova Negative relationship 2017 Li et al. Positive relationship.

Chapter 3 Methodology Research Design. Introduction Researchers utilize research designs to guide their studies. This study employs quantitative research techniques. Quantitative research collects numerical data to investigate a specific research topic..

Research Design. Objective The objective of this study is to examine the relationship between independent variables and foreign direct investment (FDI) in Malaysia. Independent variables include forest area, CO2 emissions, inflation rate, and unemployment rate..

Research Design. Researchers gather information using established instruments. Quantitative research involves a systematic method of data collection. It enables the production of statistical data for analysis..

Research Methodology. Data Collection Methodology Researchers investigated the impact of determinants on foreign direct investment (FDI) inflows into Malaysia. Data on forest area, CO2 emissions, inflation rate, and unemployment were gathered from the World Bank database. The study utilized 20 observations per year in a time series format covering the years 2000 to 2020..

Graph. Research Methodology. Use of Secondary Data All data used in the study were obtained from the World Bank database. This data is considered secondary as it was previously collected by another party for different purposes. Utilizing secondary data saves time and resources as researchers do not need to collect data individually..

Econometric model Multiple Linear Regressions. Introduction to Multiple Linear Regression Multiple linear regression models involve one dependent variable (Y) and two or more independent variables (X). The independent variables serve as explanatory variables to predict the dependent variable..

Econometric model Multiple Linear Regressions. Accuracy of Estimation Including multiple independent variables in the regression model improves the accuracy of estimation. Researchers add multiple factors to the model to ensure a reasonably accurate estimate of the dependent variable..

Economic model. FDIt= β0 + β1FAt + β2URt + β3INFt + β4AQt FDIt represents the foreign direct investment (FDI) inflows into Malaysia at time t. β0 represents the intercept term, which represents the value of FDI when all independent variables are zero. β1, β2, β3, and β4 represent the coefficients associated with the independent variables. FAt represents the forest area in Malaysia at time t. URt represents the unemployment rate in Malaysia at time t. INFt represents the inflation rate in Malaysia at time t. AQt represents the air quality in Malaysia at time t..

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Multicollinearity test. Multicollinearity arises when the independent variables inside a regression model exhibit interconnectivity rather than statistical independence. Covariance analysis is a tool used to find multicollinearity and evaluate the correlations between independent variables. The statistical importance of variables and the interpretation of results are impacted by multicollinearity, which raises the standard errors of regression coefficients..

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Graph on document with pen. Heteroscedasticity test.

Chapter 4 Research Result. Granger Causality Tests The F-value for CO2 emission causing FDI is 0.68519, and for FDI causing CO2 emission, it is 0.01779. Neither of these F-values is particularly large, indicating a weak relationship between CO2 emissions and FDI. The F-values for the unemployment rate causing FDI and FDI causing unemployment rate are 0.02765 and 0.19922, respectively. These F-values are relatively low, suggesting a weak relationship between the unemployment rate and FDI. For forest area causing FDI and FDI causing forest area, the F-values are 0.23503 and 1.33208, respectively. These F-values also indicate a weak relationship between forest area and FDI. The F-values for inflation rate causing FDI and FDI causing inflation rate are 0.90479 and 2.38907, respectively. The F-values for the other pairs of variables tested also tend to be low, indicating weak relationships between these variables in terms of Granger causality..

Heteroscedasticity Test. The probability value for the chi-square is 0.3618, the probability value for the F-statistic is 0.4048, and the value is greater than 0.01. Therefore, the null hypothesis should be rejected. Probability Obs R-squared is 4.422, this term is involving the product of the number of observations and the R-squared value. The R-squared statistic quantifies the percentage of the FDI variation that can be predicted by factors such as the unemployment rate, inflation rate, CO2 emissions, and forest area. Probability of Chi-Squared test is 0.7195 is another probability associated with the chi-squared test. The t-statistic of forest area is –1.6354 is lesser than 0.05, So, we should reject the null hypothesis. Since the unemployment rate's t-statistic is -0.1677, the null hypothesis is rejected. Since the CO2 emission t-statistic is –1.7924, the null hypothesis is rejected. Since the inflation rate's t-statistic is -0.4595, the null hypothesis is rejected..

Multicollinearity Test. The centered VIF for forest area is 1.696438, indicating a moderate degree of multicollinearity between forest area and the other independent variables in the model. The centered VIF for the unemployment rate is 1.546665, indicating a moderate degree of multicollinearity between the unemployment rate and the other independent variables in the model. The centered VIF for CO2 emission is 1.657474, indicating a moderate degree of multicollinearity between CO2 emission and the other independent variables in the model. The centered VIF for inflation rate is 1.428817, indicating a low degree of multicollinearity compared to the other independent variables in the model..

Least Square. With all other variables held constant, the coefficient of -0.196760 shows that a one-unit increase in forest area is linked to a 0.196760-unit drop in the dependent variable. With all other variables held constant, the coefficient of -1.498891 indicates that a one-unit increase in the unemployment rate is linked to a 1.498891-unit drop in the dependent variable. With all other variables held constant, the coefficient of -0.002225 indicates that a one-unit rise in CO2 emissions is correlated with a 0.002225-unit drop in FDI. With all other variables held constant, the correlation of 0.259903 shows that an increase in the inflation rate of one unit is correlated with an increase in FDI of 0.259903 units. When all independent variables are zero, the dependent variable's intercept, or baseline value, is represented by the coefficient, which is 19.20330..

Breusch-Godfrey Serial Correlation LM Test. A screenshot of a test Description automatically generated.

Wong Cheng Suan 2002986. Chapter 5. Hypotheses of the study Decision i H0: There is no relationship between all independent variables and FDI inflow in Malaysia. Do not reject Ho.

Forest area and FDI in Malaysia The relationship between the forest area and FDI in Malaysia is negative. Large-scale land acquisitions and FDI can also negatively impact the environment, leading industries to move to countries with laxer environmental laws. Inflation and FDI in Malaysia The relationship between inflation and FDI in Malaysia is negative. High inflation may discourage foreign investors by weakening foreign currency and decreasing local asset value. Continuous price increases cause significant harm to FDI. Air quality and FDI in Malaysia Air quality has a significant influence on FDI in Malaysia. Malaysia might reduce its carbon emissions while still attaining environmental sustainability through economic growth, the creation of wooded areas, and the utilization of renewable energy sources. Unemployment rate and FDI in Malaysia The unemployment rate has a significant influence on FDI in Malaysia. FDI may facilitate soft loans for industrial growth and infrastructure projects, boosting jobs and the economy..

Forest area Policymakers can use this information to formulate evidence-based policies and regulations that balance economic development objectives with environmental sustainability goals. Measures include incentives for green investments and sustainable land-use practices. Inflation Investors can use the study's findings to assess the risks and opportunities associated with FDI in Malaysia. This can enhance each other investment resilience and long-term value creation. Air quality The government should implement investments in clean technologies, renewable energy, and pollution control measures that can contribute to both environmental protection and economic growth. Policymakers can adopt stricter emissions standards to control the air quality. Unemployment rate The government should employ some policies for every sector of market development to attract more foreign direct investment and let the local people have more opportunities on the job..

The results could be impacted by methodological constraints arising from the data sources and analytical approaches employed. The accuracy and reliability of air quality data and FDI inflow statistics could vary, potentially introducing measurement errors or biases into the analysis. The study's reliance on aggregate data may obscure heterogeneity across regions or sectors, limiting the ability to capture nuanced relationships and identify specific drivers of FDI inflows and air quality outcomes. The study's focus on a specific period or a subset of variables may overlook longer-term trends or dynamics that could influence the relationship between air quality and FDI..

Researchers have the option to use monthly, quarterly, or semiannual data in place of annual data. This is because problems with autocorrelation, heteroscedasticity, and multicollinearity are less common in higher sample sizes. When using low-frequency data of this kind, it is recommended to utilize a larger sample size because there is always a risk of heteroscedasticity when using annual data. Due to the drop in data frequency, this will lessen the possibility of running into a heteroscedasticity problem. Future researchers should assess how well the current legal and policy frameworks encourage environmentally responsible investment and reduce threats to the environment. Examine the effects on FDI inflows and environmental consequences of policy measures. Future researchers can compare Malaysia's experience to other countries and examining successful investment projects can provide lessons for implementing sustainable growth..

Forest area and unemployment rate have no significant influences on FDI in Malaysia but inflation rate and air quality have a significant influence. It highlights the importance of sustainable development plans, strict environmental legislation, conservation initiatives, and ethical land-use practices in maintaining Malaysia's biodiversity and natural habitats. The findings also highlight the significance of labor market regulations, macroeconomic stability, and investment promotion tactics in attracting foreign capital and promoting long-term economic growth. The findings can guide policymakers and professionals in making informed decisions about expanding markets and investment avenues. Malaysia can accomplish its long-term economic objectives and draw in more conscientious foreign investment while policymakers must place a high priority on evidence-based decision-making and cooperative efforts. The study may offer future researchers some guidance about increasing the sample size, as this is the main cause of the challenges..

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Q & A.