• Energy Research
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This paper examines the nexus between the main forms of transport, related investments, specific air pollutants, and sustainable economic growth. The research is important since transport may act as a facilitator of social, economic, and environmental development. Based on data retrieved from Eurostat, Organisation for Economic Co-operation and Development (OECD), and World Bank, the output of fixed-effects regressions for EU-28 countries over 1990–2016 reveals that road, inland waterways, maritime, and air transport infrastructure positively influence gross domestic product per capita (GDPC), though a negative link occurred in the case of railway transport. As concerning investments in transport infrastructure, the empirical results exhibit a positive impact on economic growth for every type of transport, except inland waterways. Besides, emissions of CO2 from all kind of transport, alongside other specific air pollutants, negatively influence GDPC. The fully modified and dynamic ordinary least squares panel estimation results reinforce the findings. Further, in the short-run, Granger causality based on panel vector error correction model pointed out a unidirectional causal link running from sustainable economic growth to inland waterways and maritime transport of goods, albeit a one-way causal link running from the volume of goods transported by air to GDPC. As well, the empirical results provide support one-way short-run links running from GDPC to investments in road and inland waterway transport infrastructure. In addition, a bidirectional short-run link occurred between carbon dioxide emissions from railway transport and GDPC, whereas unidirectional relations with economic growth were identified in the case of carbon dioxide emissions from road and domestic aviation. In the long-run, a bidirectional causal relation was noticed between the length of the railways lines, investments in railway transport infrastructure, and GDPC, as well as a two-way causal link between the gross weight of seaborne goods handled in ports and GDPC.

This paper examines the nexus between the main forms of transport, related investments, specific air pollutants, and sustainable economic growth. The research is important since transport may act as a facilitator of social, economic, and environmental development. Based on data retrieved from Eurostat, Organisation for Economic Co-operation and Development (OECD), and World Bank, the output of fixed-effects regressions for EU-28 countries over 1990–2016 reveals that road, inland waterways, maritime, and air transport infrastructure positively influence gross domestic product per capita (GDPC), though a negative link occurred in the case of railway transport. As concerning investments in transport infrastructure, the empirical results exhibit a positive impact on economic growth for every type of transport, except inland waterways. Besides, emissions of CO2 from all kind of transport, alongside other specific air pollutants, negatively influence GDPC. The fully modified and dynamic ordinary least squares panel estimation results reinforce the findings. Further, in the short-run, Granger causality based on panel vector error correction model pointed out a unidirectional causal link running from sustainable economic growth to inland waterways and maritime transport of goods, albeit a one-way causal link running from the volume of goods transported by air to GDPC. As well, the empirical results provide support one-way short-run links running from GDPC to investments in road and inland waterway transport infrastructure. In addition, a bidirectional short-run link occurred between carbon dioxide emissions from railway transport and GDPC, whereas unidirectional relations with economic growth were identified in the case of carbon dioxide emissions from road and domestic aviation. In the long-run, a bidirectional causal relation was noticed between the length of the railways lines, investments in railway transport infrastructure, and GDPC, as well as a two-way causal link between the gross weight of seaborne goods handled in ports and GDPC.

To meet power quality requirements, it is necessary to classify and identify the power quality of the power grid connected with renewable energy generation. S-transform (ST) is an effective method to analyze power quality in time and frequency domains. ST is widely used to detect and classify various kinds of non-stationary power quality disturbances. However, the long taper and scaling criteria of the Gaussian window in standard ST (SST) will lead to poor time domain resolution at low frequency and poor frequency resolution at high frequency. To solve the discrete side effects, it is necessary to select the optimal window function to locate the time frequency accurately. This paper proposes a modified ST (MST) method. In this method, an improved window function of energy concentration in time-frequency distribution is introduced to optimize the shape of each window function. This method determines the parameters of Gaussian window to maximize the product of energy concentration in a time-frequency domain within a given time and frequency interval, so as to improve the energy concentration. The result shows that compared with the SST with Gaussian window, ST based on the optimally concentrated window proposed in this paper has better energy concentration in time-frequency distribution.

To meet power quality requirements, it is necessary to classify and identify the power quality of the power grid connected with renewable energy generation. S-transform (ST) is an effective method to analyze power quality in time and frequency domains. ST is widely used to detect and classify various kinds of non-stationary power quality disturbances. However, the long taper and scaling criteria of the Gaussian window in standard ST (SST) will lead to poor time domain resolution at low frequency and poor frequency resolution at high frequency. To solve the discrete side effects, it is necessary to select the optimal window function to locate the time frequency accurately. This paper proposes a modified ST (MST) method. In this method, an improved window function of energy concentration in time-frequency distribution is introduced to optimize the shape of each window function. This method determines the parameters of Gaussian window to maximize the product of energy concentration in a time-frequency domain within a given time and frequency interval, so as to improve the energy concentration. The result shows that compared with the SST with Gaussian window, ST based on the optimally concentrated window proposed in this paper has better energy concentration in time-frequency distribution.

The energy crisis and increasing fossil fuel prices due to increasing demands, controlled supplies, and global political unrest have adversely affected agricultural productivity and farm profitability across the globe and Pakistan is not an exception. To cope with this issue of energy deficiency in agriculture, the best alternate strategy is to take advantage of biomass and solid waste potential. In low-income countries such as Pakistan, the greenhouse heating system mostly relies on fossil fuels such as diesel, gasoline, and LPG. Farmers are reluctant to adopt greenhouse farming due to the continuously rising prices of the fossil fuels. To reduce reliance on fossil fuel energy, the objective of this study was to utilize biomass from crop residues to develop an efficient and economical biomass furnace that could heat greenhouses to protect the crop from seasonal temperature effects. Modifications made to the biomass furnace, such as the incorporation of insulation around the walls of the furnace, providing turbulators in fire tubes, and a secondary heat exchanger (heat recovery system) in the chimney, have increased the thermal efficiency of the biomass furnace by about 21.7%. A drastic reduction in hazardous elements of flue gases was observed due to the addition of a water scrubber smoke filter in the exit line of the flue. The efficiency of the biomass furnace ranged from 50.42% to 54.18%, whereas the heating efficiency of the diesel-fired heater was 71.19%. On the basis of the equal heating value of the fuels, the unit material and operating costs of the biomass furnace for wood, cotton stalks, corn cobs, and cow dung were USD 2.04, 1.86, 1.78, and 2.00 respectively against USD 4.67/h for the diesel heater. The capital and operating costs of the biomass furnace were about 50% and 43.7% of the diesel heater respectively, resulting in a seasonal saving of about 1573 USD. The produced smoke was tested as environmental friendly under the prescribed limits of the National Environmental Quality Standards (NEQS), which shows potential for its large-scale adoption and wider applications.

The energy crisis and increasing fossil fuel prices due to increasing demands, controlled supplies, and global political unrest have adversely affected agricultural productivity and farm profitability across the globe and Pakistan is not an exception. To cope with this issue of energy deficiency in agriculture, the best alternate strategy is to take advantage of biomass and solid waste potential. In low-income countries such as Pakistan, the greenhouse heating system mostly relies on fossil fuels such as diesel, gasoline, and LPG. Farmers are reluctant to adopt greenhouse farming due to the continuously rising prices of the fossil fuels. To reduce reliance on fossil fuel energy, the objective of this study was to utilize biomass from crop residues to develop an efficient and economical biomass furnace that could heat greenhouses to protect the crop from seasonal temperature effects. Modifications made to the biomass furnace, such as the incorporation of insulation around the walls of the furnace, providing turbulators in fire tubes, and a secondary heat exchanger (heat recovery system) in the chimney, have increased the thermal efficiency of the biomass furnace by about 21.7%. A drastic reduction in hazardous elements of flue gases was observed due to the addition of a water scrubber smoke filter in the exit line of the flue. The efficiency of the biomass furnace ranged from 50.42% to 54.18%, whereas the heating efficiency of the diesel-fired heater was 71.19%. On the basis of the equal heating value of the fuels, the unit material and operating costs of the biomass furnace for wood, cotton stalks, corn cobs, and cow dung were USD 2.04, 1.86, 1.78, and 2.00 respectively against USD 4.67/h for the diesel heater. The capital and operating costs of the biomass furnace were about 50% and 43.7% of the diesel heater respectively, resulting in a seasonal saving of about 1573 USD. The produced smoke was tested as environmental friendly under the prescribed limits of the National Environmental Quality Standards (NEQS), which shows potential for its large-scale adoption and wider applications.

Improvements in energy efficiency and production of renewable energy hold significant potential for reducing greenhouse gas emissions of housing, which accounts for 14% of global greenhouse gas emissions. In our research, we focused on the willingness of owners of detached houses to adopt renewable energy production systems of their own, and we examined perceived barriers to adopting these systems. The research was conducted using a survey and a life cycle assessment model. The survey covered three residential areas in Lahti, Finland, and the potential reductions in greenhouse gas emissions were estimated using a life cycle assessment model based on the survey results. The barriers to transformation were identified as a lack of knowledge in the following three areas: (1) the possible annual savings attained; (2) the costs of implementing energy efficiency and renewable energy production solutions; and (3) the technologies used in renewable energy production. The greenhouse gas emission reductions in the residential areas surveyed would amount to approximately 15% if the consumers implemented the solutions they considered.