PART 2 “The rise in energy consumption of rapidly growing developing countries, especially China and India, has accounted for the vast majority of the global increase in energy use in recent years....

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PART 2 “The rise in energy consumption of rapidly growing developing countries, especially China and India, has accounted for the vast majority of the global increase in energy use in recent years. Non-OECD countries currently account for approximately 60% of global energy demand, which is predicted to rise to 70% by 2040 (International Energy Agency, 2014). This increasing energy use exacerbates environmental problems including global climate change due to greenhouse gas emissions and local environmental problems such as the recent episodes of extreme air pollution in Beijing and other Chinese cities. Besides its environmental impacts, increasing energy use also raises questions of national energy supply security. As the share of world energy use consumed in developing countries increases, it is increasingly important to understand how energy use evolves across the full income continuum from less developed to highly developed countries (van Ruijven et al., 2009).” Csereklyei and Stern (2015) page 633. In this part of the home assignment we will be exploring the drivers of total and sectoral energy use across several developed and developing countries. Please use the dataset: “energy_econometrics_data_MEL1950.dta” 1. Countries have a keen interest in exploring the drivers of their sectoral energy consumption, including INDUSTRIAL energy use. Please examine the log final energy use by industry “ln_indpc” a) Design a regression model to predict “lnind_pc”. Choose the explanatory variables to include, and whether to include them as dummies/ logs/ polynomials/ interactions as you feel appropriate. Present at least two final models. (1) One with a linear per capita term (or its logs) [Model 1], (2) one with a quadratic term (or its log) [Model 2]. Which model do you think is more appropriate? How do you explain the quadratic model? Present the results of the descriptive statistics an d your final regression models in tables. You should have 3 Tables to gain full points. (3 marks) Discuss the logic behind the specification for BOTH of your models -why you used logs or levels (1 mark) -how you interpret polynomials (2 marks) -why and how you used dummies and how to interpret these (1 mark) Discuss the statistical significance of the explanatory variables in your models. (2 marks) b) Discuss how you have designed your model with reference to the “Gauss Markov” assumptions and whether these assumptions are likely to be met. (2 marks) Interpret the results of THREE of your explanatory variables including income per capita, which you consider to be the key drivers of per capita industrial energy consumption. (3 marks) (Total: 14 marks) (550 words, 3 tables, 4 calculations) There will be up to 2 additional marks awarded for presentation of your answers (neat formatting of tables and clear expression of answers in full sentences).