scholarly journals Effect of climate change on fruit by co-integration and machine learning

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tamoor Khan ◽  
Jiangtao Qiu ◽  
Ameen Banjar ◽  
Riad Alharbey ◽  
Ahmed Omar Alzahrani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Labor Force ◽  
Crop Production ◽  
Energy Use ◽  
Fruit Crops ◽  
Content Type ◽  
Fruit Crop ◽  
Agriculture Labor

Purpose The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO2 emissions, farming areas and the labor force in China. Design/methodology/approach This analysis applied the autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method and Johansen co-integration test to predict long-term co-integration and relation between variables. Four machine learning methods are used for prediction of the accuracy of climate effect on fruit production. Findings The Johansen test findings have shown that the fruit crop growth, energy use, CO2 emissions, harvested land and labor force have a long-term co-integration relation. The outcome of the long-term use of CO2 emission and rural population has a negative influence on fruit crops. The energy consumption, harvested area, total fruit yield and agriculture labor force have a positive influence on six fruit crops. The long-run relationships reveal that a 1% increase in rural population and CO2 will decrease fruit crop production by −0.59 and −1.97. The energy consumption, fruit harvested area, total fruit yield and agriculture labor force will increase fruit crop production by 0.17%, 1.52%, 1.80% and 4.33%, respectively. Furthermore, uni-directional causality is correlated with the growth of fruit crops and energy consumption. Also, the results indicate that the bi-directional causality impact varies from CO2 emissions to agricultural areas to fruit crops. Originality/value This study also fills the literature gap in implementing ARDL for agricultural fruits of China, used machine learning methods to examine the impact of climate change and to explore this important issue.

Impact of urbanization on per capita energy use and emissions in India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Stuti Haldar ◽  
Gautam Sharma
Keyword(s):  
Carbon Dioxide ◽  
Energy Consumption ◽  
Population Density ◽  
Carbon Dioxide Emissions ◽  
Energy Demand ◽  
Energy Use ◽  
Energy Intensity ◽  
Series Data ◽  
Content Type ◽  
Per Capita

Purpose The purpose of this study is to investigate the impacts of urbanization on per capita energy consumption and emissions in India. Design/methodology/approach The present study analyses the effects of urbanization on energy consumption patterns by using the Stochastic Impacts by Regression on Population, Affluence and Technology in India. Time series data from the period of 1960 to 2015 has been considered for the analysis. Variables including Population, GDP per capita, Energy intensity, share of industry in GDP, share of Services in GDP, total energy use and urbanization from World Bank data sources have been used for investigating the relationship between urbanization, affluence and energy use. Findings Energy demand is positively related to affluence (economic growth). Further the results of the analysis also suggest that, as urbanization, GDP and population are bound to increase in the future, consequently resulting in increased carbon dioxide emissions caused by increased energy demand and consumption. Thus, reducing the energy intensity is key to energy security and lower carbon dioxide emissions for India. Research limitations/implications The study will have important policy implications for India’s energy sector transition toward non- conventional, clean energy sources in the wake of growing share of its population residing in urban spaces. Originality/value There are limited number of studies considering the impacts of population density on per capita energy use. So this study also contributes methodologically by establishing per capita energy use as a function of population density and technology (i.e. growth rates of industrial and service sector).

Action for increasing energy-saving behaviour in student residences at Rhodes University, South Africa

2018 ◽  
Vol 19 (4) ◽  
pp. 773-789 ◽  
Author(s):  
Angel Ancha Lindelwa Bulunga ◽  
Gladman Thondhlana
Keyword(s):  
South Africa ◽  
Energy Consumption ◽  
Energy Saving ◽  
Behaviour Change ◽  
Green Infrastructure ◽  
Energy Demand ◽  
Energy Use ◽  
Economic Benefits ◽  
Environmental Behaviour ◽  
Content Type

Purpose In response to increasing energy demand and financial constraints to invest in green infrastructure, behaviour change energy-saving interventions are increasingly being considered as a tool for encouraging pro-environmental behaviour in campus residences. This paper aims to report on a pilot programme aimed at reducing energy consumption via behaviour change interventions, variably applied in residences at Rhodes University, South Africa. Design/methodology/approach Data were collected via structured questionnaires, energy consumption records and post-intervention programme focus group discussions. Findings Participant residences that received a mix of different interventions in the forms of pamphlets, face-to-face discussions, incentives and feedback recorded more energy reductions of up to 9 per cent than residences that received a single or no intervention. In post-experiment discussions, students cited personal, institutional and structural barriers to pro-environmental energy-use behaviour. Practical implications Overall, the results of this study suggest that information provision of energy-saving tips combined with regular feedback and incentives can result in energy-use reductions in university residences, which may yield environmental and economic benefits for universities, but addressing barriers to pro-environmental behaviour might maximise the results. Originality/value Given the lack of literature on energy conservation in the global South universities, this study provides the basis for discussing the potential for using behavioural interventions in universities for stirring pathways towards sustainability.

The dynamic relationship between CO2 emission, international tourism and energy consumption in Pakistan: a cointegration approach

2019 ◽  
Vol 74 (4) ◽  
pp. 761-779 ◽  
Author(s):  
Yaping Liu ◽  
Tafazal Kumail ◽  
Wajahat Ali ◽  
Farah Sadiq
Keyword(s):  
Economic Growth ◽  
Energy Consumption ◽  
Granger Causality ◽  
Environmental Quality ◽  
Energy Use ◽  
Ardl Model ◽  
Content Type ◽  
Dynamic Relationship ◽  
Long Run ◽  
Growth Energy

Purpose The present study aims to investigate the dynamic relationship between international tourist receipts, economic growth, energy use and carbon dioxide (CO2) emissions in Pakistan over the period 1980-2016. Many researchers have investigated the link between tourism and CO2 emissions, but there is no clear picture as the results are contradictory. This study is an attempt to compliment the literature related to tourism and environmental quality. Design/methodology/approach The study adopted the autoregressive distributed lagged (ARDL) model to investigate the short- and long-run estimates simultaneously. The study further applied Granger causality to find out the direction of causalities. To arrive at long-run robust estimates, the study used dynamic ordinary least squares (DOLS) model. Findings The results found that tourist receipts have no significant impact on environmental quality, while growth and energy consumption are the main determinants of CO2 emissions in Pakistan. The Granger causality test confirmed unidirectional causalities from GDP and energy consumption toward CO2 emissions, while tourist receipts do not affect environmental quality. DOLS technique confirmed the long-run estimates of ARDL model. Research limitations/implications The result of the study complements the literature by adding new evidence regarding the nexus of tourism and environment. Findings of the study are important for policymakers and regulatory bodies to place their focus on the development of tourism sector (services sector) rather than energy-intensive manufacturing activities to sustain the growth of the country in higher quartiles, as tourism receipts have no significant negative externalities toward environment, while energy use is one of the key determinants of environmental degradation. Originality/value This study used time series data over the period 1980-2016 for Pakistan to inspect the dynamic relationship between tourist receipts, economic growth, energy consumption and CO2 emissions.

scholarly journals Alternative Scenarios of Energy Use in U.S. Crop Production

1977 ◽  
Vol 9 (2) ◽  
pp. 9-16 ◽  
Author(s):  
Angelos Pagoulatos ◽  
John F. Timmons
Keyword(s):  
Labor Force ◽  
Crop Production ◽  
Energy Use ◽  
Agricultural Sector ◽  
Disposable Income ◽  
Export Demand ◽  
The World ◽  
Domestic Use ◽  
Alternative Scenarios ◽  
The U.S

Agriculture has been among the most productive sectors of the U.S. economy. The agricultural sector uses only four percent of the labor force to produce food needed for both domestic use and export demand. Consumers in the U.S. spend only about 17 percent of their disposable income on food, the smallest percentage of any country in the world.That energy has been recognized as the propelling force for current and continuing agricultural productivity, along with the prospect of much higher costs, have given rise to a growing interest in technologies or systems of agriculture that are less energy intensive. Possible future adjustments in agriculture may affect output levels, costs and conservation of land and water qualities.

scholarly journals Forecasting residential gas consumption with machine learning algorithms on weather data

2019 ◽  
Vol 111 ◽  
pp. 05019
Author(s):  
Brian de Keijzer ◽  
Pol de Visser ◽  
Víctor García Romillo ◽  
Víctor Gómez Muñoz ◽  
Daan Boesten ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Energy Use ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Computational Time ◽  
Percentage Error ◽  
Learning Models ◽  
Gas Consumption ◽  
Machine Learning Models

Machine learning models have proven to be reliable methods in the forecasting of energy use in commercial and office buildings. However, little research has been done on energy forecasting in dwellings, mainly due to the difficulty of obtaining household level data while keeping the privacy of inhabitants in mind. Gaining insight into the energy consumption in the near future can be helpful in balancing the grid and insights in how to reduce the energy consumption can be received. In collaboration with OPSCHALER, a measurement campaign on the influence of housing characteristics on energy costs and comfort, several machine learning models were compared on forecasting performance and the computational time needed. Nine months of data containing the mean gas consumption of 52 dwellings on a one hour resolution was used for this research. The first 6 months were used for training, whereas the last 3 months were used to evaluate the models. The results showed that the Deep Neural Network (DNN) performed best with a 50.1 % Mean Absolute Percentage Error (MAPE) on a one hour resolution. When comparing daily and weekly resolutions, the Multivariate Linear Regression (MVLR) outperformed other models, with a 20.1 % and 17.0 % MAPE, respectively. The models were programmed in Python.

Considering nonlinearity and structural changes in the convergence of clean energy consumption: the case of OECD countries

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Veli Yilanci ◽  
Muhammed Sehid Gorus
Keyword(s):  
Energy Consumption ◽  
Unit Root ◽  
Energy Use ◽  
Clean Energy ◽  
Oecd Countries ◽  
Unit Root Tests ◽  
Panel Unit Root Tests ◽  
Stochastic Convergence ◽  
Panel Unit Root ◽  
Content Type

PurposeIn this study, we aim to test the stochastic convergence of per capita clean energy use in 30 OECD (Organization for Economic Co-operation and Development) countries for the period of 1965–2017.Design/methodology/approachThis study employed both linear and nonlinear panel unit root tests, and unlike other studies, this study allowed fractional values in addition to integer values for frequencies in the Fourier functions. Integer values of frequency indicate temporary breaks, while fractional values show permanent breaks.FindingsThe results of the linear panel unit root test indicate that clean energy use does not converge to group average for almost all OECD countries. However, the results of nonlinear panel unit root tests provide evidence that the stochastic convergence hypothesis of clean energy consumption cannot be rejected for most countries. This study does not find any evidence for stochastic convergence of clean energy use in Australia, Canada, Denmark, Ireland, Norway or Sweden. Therefore, the policies regarding clean energy are mandatory in these countries due to their effectiveness. This study also reveals that there are permanent structural breaks in the convergence process of clean energy consumption in approximately half of OECD countries.Originality/valueThis study considers temporary and permanent smooth structural shifts in addition to nonlinearity when testing the stationarity of clean energy consumption in a country i relative to the group average. This new method eliminates deficiencies of the previous panel data techniques. Thus, it provides more reliable results compared to existing literature.

scholarly journals The Long-Term Trend Analysis and Scenario Simulation of the Carbon Price Based on the Energy-Economic Regulation

2020 ◽  
Vol 12 (5) ◽  
pp. 653-668
Author(s):  
Zhao-Peng Li ◽  
Li Yang ◽  
Si-Rui Li ◽  
Xiaoling Yuan
Keyword(s):  
Economic Development ◽  
Energy Consumption ◽  
Clean Energy ◽  
Carbon Price ◽  
Energy Development ◽  
Carbon Market ◽  
Content Type ◽  
Price Trends ◽  
The Impact

Purpose China’s national carbon market will be officially launched in 2020, when it will become the world’s largest carbon market. However, China’s carbon market is faced with various major challenges. One of the most important challenges is its impact on the social and economic development of arid and semi-arid regions. By simulating the carbon price trends under different economic development and energy consumption levels, this study aims to help the government can plan ahead to formulate various countermeasures to promote the integration of arid and semi-arid regions into the national carbon market. Design/methodology/approach To achieve this goal, this paper builds a back propagation neural network model, takes the third phase of the European Union Emissions Trading System (EU ETS) as the research object and uses the mean impact value method to screen out the important driving variables of European Union Allowance (EUA) price, including economic development (Stoxx600, Stoxx50, FTSE, CAC40 and DAX), black energy (coal and Brent), clean energy (gas, PV Crystalox Solar and Nordex) and carbon price alternatives Certification Emission Reduction (CER). Finally, this paper sets up six scenarios by combining the above variables to simulate the impact of different economic development and energy consumption levels on carbon price trends. Findings Under the control of the unchanged CER price level, economic development, black energy and clean energy development will all have a certain impact on the EUA price trends. When economic development, black energy consumption and clean energy development are on the rise, the EUA price level will increase. When the three types of variables show a downward trend, except for the sluggish development of clean energy, which will cause the EUA price to rise sharply, the EUA price trend will also decline accordingly in the remaining scenarios. Originality/value On the one hand, this paper incorporates driving factors of carbon price into the construction of carbon price prediction system, which not only has higher prediction accuracy but also can simulate the long-term price trend. On the other hand, this paper uses scenario simulation to show the size, direction and duration of the impact of economic development, black energy consumption and clean energy development on carbon prices in a more intuitive way.

Long-Term Assessment Methodology of Building Stock Thermal Energy Consumption

2021 ◽  
Author(s):  
◽  
Aleksejs Prozuments
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Thermal Energy ◽  
Energy Use ◽  
Energy Savings ◽  
Building Code ◽  
Space Heating ◽  
Industrial Building ◽  
Building Stock

Energy efficiency in the building stock is a substantial contributor to infrastructure sustainability. In Latvia, buildings’ thermal energy use for space heating accounts for 80 % of total building energy use in the cold season. Therefore, reducing thermal energy consumption for space heating needs through the implementation of energy efficiency measures, enforcement of local building codes and regulations can ultimately lead to cost savings for building owners and stakeholders. The present PhD Thesis introduces a methodology for evaluation of thermal energy saving potential in the long run across residential, public, and industrial building stock under various thermal energy consumption compliance scenarios. These scenarios were developed based on three different building code protocols with a 10-year forecast analysis. Evaluation of the proposed building code implementation practices and their feasibility in Latvian building stock is discussed for these buildings with regards to their long-term thermal energy savings potential.

A novel adaptive discrete grey prediction model for forecasting development in energy consumption structure—from the perspective of compositional data

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wuyong Qian ◽  
Hao Zhang ◽  
Aodi Sui ◽  
Yuhong Wang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Compositional Data ◽  
Development Trend ◽  
Grey Prediction ◽  
Content Type ◽  
Consumption Structure ◽  
Grey Prediction Model ◽  
Proposed Model

PurposeThe purpose of this study is to make a prediction of China's energy consumption structure from the perspective of compositional data and construct a novel grey model for forecasting compositional data.Design/methodology/approachDue to the existing grey prediction model based on compositional data cannot effectively excavate the evolution law of correlation dimension sequence of compositional data. Thus, the adaptive discrete grey prediction model with innovation term based on compositional data is proposed to forecast the integral structure of China's energy consumption. The prediction results from the new model are then compared with three existing approaches and the comparison results indicate that the proposed model generally outperforms existing methods. A further prediction of China's energy consumption structure is conducted into a future horizon from 2021 to 2035 by using the model.FindingsChina's energy structure will change significantly in the medium and long term and China's energy consumption structure can reach the long-term goal. Besides, the proposed model can better mine and predict the development trend of single time series after the transformation of compositional data.Originality/valueThe paper considers the dynamic change of grey action quantity, the characteristics of compositional data and the impact of new information about the system itself on the current system development trend and proposes a novel adaptive discrete grey prediction model with innovation term based on compositional data, which fills the gap in previous studies.

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