Integrated Analysis of CO2 Emission Intensity, Bilateral FDI, and Institutional Quality Nexus: Case Study of G20 Countries

A clean natural environment is a primary concern of contemporary lives, business investments, and governments. However, there is a lack of knowledge of how countries can achieve high investment across borders and better institutional quality while protecting the environment. Thus the current paper explores the effect of bilateral FDI, institutional quality, and CO2 emission intensity on each other for 19 selected G20 countries over the 2009-2017 periods. This paper estimates the three equations that jointly address the endogeneity problem by employing both static and dynamic simultaneous econometrics techniques with a panel dataset. The empirical results confirm that bilateral FDI reduces CO2 emission intensity and strengthens the institutional quality in G20. The results also support a positive and significant effect of institutional quality on bilateral inward FDI and CO2 emission intensity. This paper confirms a positive and considerable feedback effect of CO2 emission intensity on institutional quality. Further, this study establishes a triplex relationship between these three factors and consolidates vital policy insights to achieve sustainable growth concerning the nexus among environment quality-FDI-institution quality for G20 economies.

The Impacts of Non-Renewable Energy Consumption and Education Expenditure on CO2 Emission Intensity of Real GDP in China

BackgroundWith the economic development, China has become the world's largest CO2 emitter. Given that climate warming has increasingly become the focus of the international community, Chinese government committed to reducing its CO2 emission intensity substantially. Prior studies find that the evolution of economic structure and technological progress can reduce CO2 emissions, but lack of considering CO2 emissions and output as a whole. In addition, the role of education expenditure is relatively overlooked. This paper contributes to the literature by examining the link of CO2 emission intensity, non-renewable energy consumption and education expenditure in China during 1971-2014. ResultsWe use the ARDL approach and find that in the long run, every 1% increase in non-renewable energy consumption results in a 0.92% increase in CO2 intensity, while every 1% increase in operational education expenditure reduces the CO2 intensity by 0.86%. In the short term, 36% of the deviation from the long run equilibrium is corrected in the next period.ConclusionsWe draw out two important conclusions and make important policy recommendations. First and foremost, as long as the increase in operational educational expenditure exceeds the increase in non-renewable energy consumption, CO2 intensity of real GDP will decrease in the long run. This means that in the development stage when economic activities are still highly dependent on non-renewable energy sources, the Chinese government should continue to vigorously increase expenditures on public education. Second, the increase in non-renewable energy consumption will result in an increase in CO2 intensity of real GDP. Therefore, gradually increasing the proportion of clean energy consumption in the energy nexus is another powerful starting point for China to achieve its goal of reducing CO2 intensity of real GDP.JEL ClassificationC32. I2. Q4. Q53. Q56.

Comparative Study of Induction Motors of IE2, IE3 and IE4 Efficiency Classes in Pump Applications Taking into Account CO2 Emission Intensity

The high energy intensity of the modern industry and the threat of climate change determine the high urgency of increasing the energy efficiency of electric motors. In this paper, energy consumption, energy costs, payback periods, and CO2 emissions of 75 kW, 4 pole induction motors with direct grid supply in a fixed-speed pump unit are evaluated. Motors of the IE2, IE3, and IE4 efficiency classes according to IEC 60034-30-1 standard are compared in terms of life-time energy savings, payback period, and CO2 emissions. To carry out the analysis, polynomial interpolation of the data from the available manufacturer datasheets of the motors is used. It concluded that even though the initial investment cost of the IE4-motor is higher than that of IE3-motor, the IE4-motor is more profitable if more than 3 years of operation are considered and also provides significant reductions of CO2 emissions. The paper presents a calculation method of the aforementioned indicators which can be useful for companies, researchers, and engineers for quick assessment and selection of technical solutions.

Will China Achieve Its Ambitious Goal?—Forecasting the CO2 Emission Intensity of China towards 2030

China has set out an ambitious target of emission abatement; that is, a 60–65% reduction in CO2 emission intensity by 2030 compared with the 2005 baseline level and emission peak realisation. This paper aimed to forecast whether China can fulfil the reduction target of CO2 emission intensity and peak by 2030 based on the historical time series data from 1990 to 2018. Four different forecasting techniques were used to improve the accuracy of the forecasting results: the autoregressive integrated moving average (ARIMA) model and three grey system-based models, including the traditional grey model (1,1), the discrete grey model (DGM) and the rolling DGM. The behaviours of these techniques were compared and validated in the forecasting comparisons. The forecasting performance of the four forecasting models was good considering the minimum mean absolute percentage error (MAPE), demonstrating MAPE values lower than 2%. ARIMA showed the best forecasting performance over the historical period with a MAPE value of 0.60%. The forecasting results of ARIMA indicate that China would not achieve sufficient reductions despite its projected emission peak of 96.3 hundred million tons by 2021. That is, the CO2 emission intensity of China will be reduced by 57.65% in 2030 compared with the 2005 levels. This reduction is lower than the government goal of 60–65%. This paper presents pragmatic recommendations for effective emission intensity reduction to ensure the achievements of the claimed policy goals.

Control of Heat Pumps with CO2 Emission Intensity Forecasts

An optimized heat pump control for building heating was developed for minimizing CO 2 emissions from related electrical power generation. The control is using weather and CO 2 emission forecasts as inputs to a Model Predictive Control (MPC)—a multivariate control algorithm using a dynamic process model, constraints and a cost function to be minimized. In a simulation study, the control was applied using weather and power grid conditions during a full-year period in 2017–2018 for the power bidding zone DK2 (East, Denmark). Two scenarios were studied; one with a family house and one with an office building. The buildings were dimensioned based on standards and building codes/regulations. The main results are measured as the CO 2 emission savings relative to a classical thermostatic control. Note that this only measures the gain achieved using the MPC control, that is, the energy flexibility, not the absolute savings. The results show that around 16% of savings could have been achieved during the period in well-insulated new buildings with floor heating. Further, a sensitivity analysis was carried out to evaluate the effect of various building properties, for example, level of insulation and thermal capacity. Danish building codes from 1977 and forward were used as benchmarks for insulation levels. It was shown that both insulation and thermal mass influence the achievable flexibility savings, especially for floor heating. Buildings that comply with building codes later than 1979 could provide flexibility emission savings of around 10%, while buildings that comply with earlier codes provided savings in the range of 0–5% depending on the heating system and thermal mass.