Business Analytics – Renewable Energy & GDP Growth Analysis

Case study

Energy plays a central role in economic development, industrial productivity, and improvements in living standards. At the same time, rising energy demand and dependence on fossil fuels contribute significantly to carbon emissions and climate change. In response, many countries, including Malaysia, have increased focus on renewable energy adoption as part of broader sustainable development and energy transition strategies.

Renewable energy can contribute to long-term economic growth, improve energy security, reduce environmental degradation, and support climate commitments. However, the effectiveness of renewable energy policies may be influenced by other macroeconomic and structural factors such as income growth, electricity demand, urbanization, and carbon emissions. Understanding how these variables interact is important for evaluating policy effectiveness and informing future development planning.

You are interested in learning more about the determinants of renewable energy dynamics and their relationship with economic growth in Malaysia. To understand this better, please do some reading on these topics before you start the assignment. The variables you need to use are given below. The full dataset is available in Appendix 1_EnergyMalaysia on the assessment page.

Variables:

a) Renewable energy consumption (% of total final energy consumption).
b) GDP per capita (constant 2015 US$).
c) Electric power consumption (kWh per capita).
d) CO₂ emissions (metric tons per capita).
e) Urban population.

Question 1.A

Understanding deforestation: Preliminary work

Work Required: Prior to answering all the questions, you are expected to clean the data set in the excel sheet by removing all missing observations (including values recorded as ..)., converting all the values of the variables to LN (Natural Logarithm) form, Renaming variables using one word or underscore format for example (LN RenewableEnergy or LN_RenewableEnergy).

In preparing your answers to all questions (from question 2.A onwards), only use the cleaned data. Note that you are required to save the cleaned data set and submit it as a separate file with your assessment.

Question 2.A

Performing Diagnostic Tests

Work Required: Provide a comprehensive explanation and application of the following diagnostic tests using the cleaned dataset:

• Multicollinearity Diagnostic Test: Evaluate whether relationships among Renewable energy consumption, GDP per capita, Electricity consumption, CO₂ emissions, Urban population create multicollinearity issues and interpret the Correlation matrix, VIF and tolerance statistics, and any problematic relationships.
• Serial Correlation Test: Conduct appropriate serial correlation diagnostics (for example Durbin-Watson or Breusch-Godfrey tests). Interpret whether time-dependent autocorrelation exists in the economic-environmental data and discuss implications.
• Heteroskedasticity Test: Perform and interpret heteroskedasticity diagnostics. Discuss whether unequal variance affects coefficient reliability, inference, and forecasting accuracy.

Please, insert all the output tables generated through SPSS. Justify your answer with evidence from a minimum of two (2) relevant literature for each test, to justify the importance of these tests.

Question 3.A

Addressing Inconsistencies in the Data

Work Required: For each issue identified in Question 2A, suggest at least one corrective measure for each test. Justify your answer with evidence from a minimum of two (2) relevant literature for each suggestion.

Please ensure to address the following aspects: quality of sentencing and grammar, usage of clear structure, relevance of your justifications.

Question 4.A

Applying distributed lag models

Work Required: You are expected to Introduce the concept of distributed lag models and explain how they can be applied in economic and environmental analysis.

In preparing your answer you must implement a distributed lag model on the Renewable Energy Consumption and GDP per capita for the last 3 years. Analyze the lagged short-run and delayed impacts of renewable energy on GDP and provide at least two (2) insights.

Please, insert all the output tables generated through software in your answer. Justify each insight with evidence from a minimum of one (1) relevant literature and ensure to address the following aspects: quality of sentencing and grammar, usage of clear structure.

Question 5.A

Benefit and risk assessment of dynamic model

Work Required: You are expected to explain how dynamic models could help forecast future GDP per capita using historical data and relevant predictors such as, renewable energy consumption trends, urban population growth, electricity demand, or CO₂ emissions patterns.

In preparing your answer you are expected to discuss three (3) benefits of dynamic models and consider any three (3) challenges/risks that may arise in implementing dynamic models.

Please, justify your answer with evidence from a minimum of two (2) relevant literatures and ensure to address the following aspects: quality of sentencing and grammar, usage of clear structure.

Experts Answer on Above Question on Business Analytics Question

Performing Diagnostic tests

Multicollinearity Diagnostic test – The results of the correlation Matrix are indicated below:

The vif and tolerance results are shown below:

Interpretation – An analysis of the findings above indicates a moderate to high correlation among GDP per capita, energy consumption and Urban population. The coordination between the GDP per capita and urban population is significantly high at .926 which indicates the existence of a strong linear relationship.
The value in respect to VIF is below the critical threshold of 10 and the tolerance value exceeds 0.10 whith indicates the absence of severe multicollinearity. Multicollinearity is essential because it inflates standard errors and reduces the precision of coefficient estimates. The findings therefore indicate that no severe multicollinearity problem exists, but it is essential to maintain adequate focus when interpreting GDP and urbanisation coefficients.

Serial correlation test

The Durbin Watson result shows a correlation of 1.52 which is well below the ideal value of 2.0. It implies the existence of positive first order autocorrelation. This result is quite viable because GDP, energy consumption and urbanisation indicates the existence of long term trends. However the serial correlation is problematic because OLS standard errors become biased, which accounts for unreliable hypothesis testing. The overall finding suggests evidence of positive serial correlations.

Heteroskedasticity test

The analysis using Breusch Pagan Test indicates the chi-square value of 9.81 and p-value of .044. As the P value is well below 0.05, it implies that the null hypothesis of homoscedasticity is rejected and it ultimately indicates the presence of heteroskedasticity. The variance of regression residual changes over time such as reflecting structural changes in Malaysia’s economy, energy sector reforms and economic growth. The finding therefore indicates that the model exhibits heteroskedasticity and corrective measures are recommended.

Addressing inconsistencies in the data

With respect to multicollinearity, the adjustments made were the removal of highly correlated variables, and one variable has been excluded from the GDP per capita and Urban population, as they reflect strong correlation. In serial correlation, auto regulasive models have been applied, and lagged GDP terms were added in order to capture persistence in economic growth. In respect to heteroskedasticity, robust standard errors were applied to get valid statistical inference when error variance is unequal.

Applying distributed lag models

The role and implication of a distributed lag model is to evaluate the effect of an independent variable over a dependent variable during several periods rather than one contemporaneously. In respect to Malaysia, the renewable energy investment may not reflect the GDP immediately because the development of infrastructure, technology adaptation and policy implementation need time.

Benefits and risk of dynamic models

The main benefits are improved forecast accuracy and they are also significant in capturing delayed economic effects. The dynamic models also help in better evaluating the policy and thereby assist in the decision making process.
However the risks are mainly in the form of model specification errors, data quality problems and structural breaks.

Want Detailed Answers with References?

The analysis above in relation to Malaysia’s renewable energy and GDP econometrics including SPSS analysis, diagnostic test, distributed lag models and forecasting revealed significant findings. If you need assistance with similar types of economic assignment involving the application of statistical techniques, consult our economics assignment helpers to get positive support.