Nexus between Energy Usability, Economic Indicators and Environmental Sustainability in Four ASEAN Countries: A Non-Linear Autoregressive Exogenous Neural Network Modelling Approach

This study investigates the use of a non-linear autoregressive exogenous neural network (NARX) model to investigate the nexus between energy usability, economic indicators, and carbon dioxide (CO2) emissions in four Association of South East Asian Nations (ASEAN), namely Malaysia, Thailand, Indonesia, and the Philippines. Optimized NARX model architectures of 5-29-1, 5-19-1, 5-17-1, 5-13-1 representing the input nodes, hidden neurons and the output units were obtained from the series of models configured. Based on the relationship between the input variables, CO2 emissions were predicted with a high correlation coefficient (R) > 0.9. and low mean square errors (MSE) of 3.92 × 10−21, 4.15 × 10−23, 2.02 × 10−19, 1.32 × 10−20 for Malaysia, Thailand, Indonesia, and the Philippines, respectively. Coal consumption has the highest level of influence on CO2 emissions in the four ASEAN countries based on the sensitivity analysis. These findings suggest that government policies in the four ASEAN countries should be more intensified on strategies to reduce CO2 emissions in relationship with the energy and economic indicators.

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Analisis Komparasi Hapiness Index 5 Negara di Asean

Jurnal Ekonomi Pembangunan ◽

10.23960/jep.v9i2.79 ◽

2020 ◽

Vol 9 (2) ◽

pp. 56-65

Author(s):

I Wayan Suparta ◽

Rizka Malia

Keyword(s):

Poverty Reduction ◽

Positive Impact ◽

The Philippines ◽

Economic Indicators ◽

Gdp Per Capita ◽

Environmental Index ◽

Personal Impact ◽

Per Capita ◽

Asean Countries ◽

The Right

The limitation of economic indicators in representing the level of community welfare has increased the world's attention to social aspects of development. Development progress, which has been seen more by economic indicators, such as economic growth and poverty reduction, is considered insufficient to reflect the right level of welfare. This study aims to determine the effect of GDP per capita, environmental index, and unemployment on the happiness index of 9 countries in ASEAN. Estimation results show that the variable GDP per capita significantly and negatively influences the happiness index. The environmental index has a positive effect on the Happiness Index, and unemployment has a positive impact on the happiness index. Based on the results of special effects, there are individual effect values in 9 ASEAN countries. Singapore is the country with the most significant personal impact, and the Philippines is the country with the smallest particular effect.

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Maximum and minimum temperature prediction over western Himalaya using artificial neural network

MAUSAM ◽

10.54302/mausam.v63i2.1423 ◽

2021 ◽

Vol 63 (2) ◽

pp. 283-290

Author(s):

PIYUSH JOSHI ◽

A. GANJU

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Minimum Temperature ◽

Linear Method ◽

Western Himalaya ◽

Temperature Prediction ◽

Data Set ◽

Non Linear ◽

Artificial Neural ◽

Mean Square Errors

Due to eastward moving synoptic weather system called Western Disturbance (WD), Western Himalaya receives enormous amount of precipitation in the form of snow during winter months (November to April). This precipitation keeps on accumulating and poses an avalanche threat. Temperature plays an important role for the initiation of avalanches. Therefore, prediction of maximum and minimum temperature may be quite helpful for avalanche forecasting. In the present study Artificial Neural Network (ANN), a non-linear method is used for the prediction of maximum and minimum temperature using surface meteorological data observed at various observatories in Western Himalaya region. ANN provides a computational efficient way of determining an empirical possible non-linear relationship between a number of input and one or more outputs. In present study back propagation learning algorithm is used to train the network. In the training process the relationship between input and output is extracted i.e., final weights are computed. Past data of about 25 years is used for training the network and trained network is used for temperature prediction for five winter seasons (2005-06 to 2009-10). Root mean square errors (RMSE) corresponding to maximum and minimum temperature are computed. For independent data set RMSE vary from 2.18 to 2.48 and 1.99 to 2.78 for maximum and minimum temperatures respectively.

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Investigating the Dynamic Impact of CO2 Emissions and Economic Growth on Renewable Energy Production: Evidence from FMOLS and DOLS Tests

Processes ◽

10.3390/pr7080496 ◽

2019 ◽

Vol 7 (8) ◽

pp. 496 ◽

Cited By ~ 5

Author(s):

Khan ◽

Panigrahi ◽

Almuniri ◽

Soomro ◽

Mirjat ◽

...

Keyword(s):

Economic Growth ◽

Renewable Energy ◽

Co2 Emissions ◽

Energy Production ◽

Clean Energy ◽

The Philippines ◽

Least Square ◽

Ordinary Least Square ◽

Renewable Energy Production ◽

Asean Countries

Understanding the dynamic nexus between CO2 emissions and economic growth in the sustainable environment helps the economies in developing resources and formulating apposite energy policies. In the recent past, various studies have explored the nexus between CO2 emissions and economic growth. This study, however, investigates the nexus between renewable energy production, CO2 emissions, and economic growth over the period from 1995 to 2016 for seven Association of Southeast Asian Nations (ASEAN) countries. Fully Modified Ordinary Least Square (FMOLS) and Dynamic Ordinary Least Square (DOLS) methodologies were used to estimate the long- and short-run relationships. The panel results revealed that renewable energy production has a significant long term effect on CO2 emissions for Vietnam (t = −2.990), Thailand (t = −2.505), and Indonesia (t = −2.515), and economic growth impact for Malaysia (t = 2.050), Thailand (t = −2.001), and the Philippines (t = −2.710). It is, therefore, vital that the ASEAN countries implement policies and strategies that ensure energy saving and continuous economic growth without forsaking the environment. This study, as such, recommends that ASEAN countries should take measures to decrease the reliance on fossil fuels for achieving these objectives. Future research should consider the principles of circular economy and clean energy development mechanisms integrated with renewable energy technologies.

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PRO-HUMAN ECONOMIC INDICATORS AND THEIR RELATIONSHIP WITH ENVIRONMENTAL SUSTAINABILITY IN ASEAN COUNTRIES: ANALYZING HUMAN CAPITAL INVESTMENT, BRAIN DRAIN AND IMMIGRATION THROUGH PANEL DATA

Journal of Security and Sustainability Issues ◽

10.9770/jssi.2020.10.oct(29) ◽

2020 ◽

Vol 10 (Oct) ◽

pp. 360-371

Author(s):

Soheil Kazemian ◽

Ahmed Dhubaibi ◽

Norlaila Md Zin ◽

Zuraidah Mohd Sanusi ◽

Zalina Zainudin

Keyword(s):

Human Capital ◽

Panel Data ◽

Brain Drain ◽

Environmental Sustainability ◽

Capital Investment ◽

Human Capital Investment ◽

Economic Indicators ◽

Asean Countries

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Do Public–Private Partnership Investment in Energy and Technological Innovation Matter for Environmental Sustainability in the East Asia and Pacific Region? An Application of a Frequency Domain Causality Test

Sustainability ◽

10.3390/su13063039 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3039

Author(s):

Tomiwa Sunday Adebayo ◽

Sema Yılmaz Genç ◽

Rui Alexandre Castanho ◽

Dervis Kirikkaleli

Keyword(s):

Renewable Energy ◽

Energy Consumption ◽

Frequency Domain ◽

Co2 Emissions ◽

Technological Innovation ◽

Environmental Sustainability ◽

Public Private Partnership ◽

Renewable Energy Consumption ◽

Causality Test ◽

Private Partnership

Environmental sustainability is an important issue for current scholars and policymakers in the East Asian and Pacific region. The causal and long-run effects of technological innovation, public–private partnership investment in energy, and renewable energy consumption on environmental sustainability in the East Asian and Pacific regions have not been comprehensively explored while taking into account the role of economic growth using quarterly data for the period 1992–2015. Therefore, the present study aims to close this literature gap using econometric approaches, namely Bayer–Hanck cointegration, autoregressive distributed lag (ARDL), dynamic ordinary least square (DOLS), and fully modified ordinary least square (FMOLS) tests. Furthermore, the study utilizes the frequency domain causality test to capture the causal impact of public–private partnership investment in energy, renewable energy consumption, technological innovation, and economic growth on CO2 emissions. The advantage of the frequency domain causality test is that it can capture the causality between short-term, medium-term, and long-term variables. The outcomes of the ARDL, FMOLS and DOLS show that renewable energy consumption and technological innovation mitigate CO2 emissions, while public–private partnership investment in energy and economic growth increase CO2 emissions. Moreover, the frequency causality test outcomes reveal that technological innovation, public–private partnership investment in energy, and renewable energy consumption cause CO2 emissions, particularly in the long-term. Thus, as a policy recommendation, the present study recommends promoting renewable energy consumption by focusing more on technological innovation in the East Asia and Pacific regions.

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A Hybrid Hidden Markov Model for Pipeline Leakage Detection

Applied Sciences ◽

10.3390/app11073138 ◽

2021 ◽

Vol 11 (7) ◽

pp. 3138

Author(s):

Mingchi Zhang ◽

Xuemin Chen ◽

Wei Li

Keyword(s):

Neural Network ◽

Markov Model ◽

Hidden Markov Model ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Deep Neural Network ◽

Hidden Markov ◽

Gaussian Mixture ◽

State Sequence ◽

Non Linear

In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). The hybrid HMM, i.e., DNN-HMM, consists of a deep neural network (DNN) with multiple layers to exploit the non-linear data. The DNN is initialized by using a deep belief network (DBN). The DBN is a pre-trained model built by stacking top-down restricted Boltzmann machines (RBM) that compute the emission probabilities for the HMM instead of Gaussian mixture model (GMM). Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. The accuracy of the testing performance between detected state sequence and actual state sequence is measured by micro F1 score. The micro F1 score approaches 0.94 for GMM-HMM method and it is close to 0.95 for DNN-HMM method when the pipeline is divided into three sections. In the experiment that divides the pipeline as five sections, the micro F1 score for GMM-HMM is 0.69, while it approaches 0.96 with DNN-HMM method. The results demonstrate that the DNN-HMM can learn a better model of non-linear data and achieve better performance compared to GMM-HMM method.

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Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques

International Journal of Knowledge-based and Intelligent Engineering Systems ◽

10.3233/kes-210066 ◽

2021 ◽

Vol 25 (2) ◽

pp. 227-234

Author(s):

Sandeep Samantaray ◽

Abinash Sahoo

Keyword(s):

Neural Network ◽

Mean Square Error ◽

Water Table ◽

Environmental Sustainability ◽

Model Development ◽

Water Table Depth ◽

Water Levels ◽

Coefficient Of Determination ◽

Mean Square ◽

Inference System

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.

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Does Green Energy Complement Economic Growth for Achieving Environmental Sustainability? Evidence from Saudi Arabia

Sustainability ◽

10.3390/su13010180 ◽

2020 ◽

Vol 13 (1) ◽

pp. 180

Author(s):

Montassar Kahia ◽

Anis Omri ◽

Bilel Jarraya

Keyword(s):

Economic Growth ◽

Saudi Arabia ◽

Renewable Energy ◽

Energy Consumption ◽

Co2 Emissions ◽

Environmental Sustainability ◽

Least Square ◽

Ekc Hypothesis ◽

Renewable Energy Consumption ◽

Ordinary Least Square

This study extends previous environmental sustainability literature by investigating the joint impact of economic growth and renewable energy on reducing CO2 emissions in Saudi Arabia over the period 1990–2016. Using the fully modified ordinary least-square (FMOLS) and dynamic ordinary least-square DOLS estimators, we find that economic growth increases CO2 emissions in all estimated models. Moreover, the validity of the environmental Kuznets curve (EKC) hypothesis is only supported for CO2 emissions from liquid fuel consumption. The invalidity of the EKC hypothesis in the most commonly used models implies that economic growth alone is not sufficient to enhance environmental quality. Renewable energy is found to have a weak influence on reducing the indicators of environmental degradation. We also find that the joint impact of renewable energy consumption and economic growth on the indicators of CO2 emissions is negative and insignificant for all the estimated models, meaning that the level of renewable energy consumption in Saudi Arabia is not sufficient to moderate the negative effect of economic growth on environmental quality. Implications for policy are also discussed.

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Dominance of Fossil Fuels in Japan’s National Energy Mix and Implications for Environmental Sustainability

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18147347 ◽

2021 ◽

Vol 18 (14) ◽

pp. 7347

Author(s):

Tomiwa Sunday Adebayo ◽

Abraham Ayobamiji Awosusi ◽

Seun Damola Oladipupo ◽

Ephraim Bonah Agyekum ◽

Arunkumar Jayakumar ◽

...

Keyword(s):

Economic Growth ◽

Sustainable Development ◽

Natural Gas ◽

Co2 Emissions ◽

Environmental Sustainability ◽

Least Square ◽

Kuznets Curve ◽

Gas Oil ◽

Energy Mix ◽

Ordinary Least Square

Despite the drive for increased environmental protection and the achievement of the Sustainable Development Goals (SDGs), coal, oil, and natural gas use continues to dominate Japan’s energy mix. In light of this issue, this research assessed the position of natural gas, oil, and coal energy use in Japan’s environmental mitigation efforts from the perspective of sustainable development with respect to economic growth between 1965 and 2019. In this regard, the study employs Bayer and Hanck cointegration, fully modified Ordinary Least Square (FMOLS), and dynamic ordinary least square (DOLS) to investigate these interconnections. The empirical findings from this study revealed that the utilization of natural gas, oil, and coal energy reduces the sustainability of the environment with oil consumption having the most significant impact. Furthermore, the study validates the environmental Kuznets curve (EKC) hypothesis in Japan. The outcomes of the Gradual shift causality showed that CO2 emissions can predict economic growth, while oil, coal, and energy consumption can predict CO2 emissions in Japan. Given Japan’s ongoing energy crisis, this innovative analysis provides valuable policy insights to stakeholders and authorities in the nation’s energy sector.

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