Energy-environment-economy causal nexus in Pakistan: A graph theoretic approach

Fazal, Rizwan; Rehman, Syed Aziz Ur; Rehman, Atiq Ur; Bhatti, M. Ishaq; Hussain, Anwar

doi:10.1016/j.energy.2020.118934

Cited by 22 publications

(15 citation statements)

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“…In recent decades, concern has grown over the issues, especially when the resilience and shock absorbing ability of the environment fail to go with the trend of economic growth [35]. The release of GHGs in the atmosphere is a clear example of a negative externality of economic growth that levies a substantial burden on a global scale [16,32]. In the language of economic theory, the current market for carbonbased energies such as coal, oil and natural gas considers only private gains and welfares, which leads to a market equilibrium that does not match the social optimum.…”

Section: Literature Reviewmentioning

confidence: 99%

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Global Oil Price and Innovation for Sustainability: The Impact of R&D Spending, Oil Price and Oil Price Volatility on GHG Emissions

et al. 2021

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Recently, sustainable economic growth has taken the front line of the global development agenda. The common dependency on fossil fuel energy, greenhouse gas (GHG) emissions and the continuous rising demands for energy have posed challenges that put the world in a climate change trap. This work empirically analyzes the effect of innovation, oil price, oil price volatility and economic growth on GHG emissions over the period of 1991–2015. The study compares the emission level between European Union countries (EU) (26), oil-producing countries (22), China and the United States of America (USA) using the Driscoll–Kraay model. The main empirical finding points to a positive effect of innovation on GHG emission reduction initiatives in oil-importing economies. Particularly, EU countries significantly minimized emissions due to innovation, followed by China and the USA. Contrarily, the effect of innovation increases GHG emission in oil-exporting economies. The results also indicate broader significant effects of oil price and oil price volatility on GHG emission. Interestingly, the effect of oil price on GHG emission is asymmetrical between oil-exporting and -importing economies. Oil price increases in oil-importing countries decrease GHG emission; contrarily, its effect increases emissions in oil-exporting countries. Thus, oil-exporting countries lack motivation to decrease emission levels due to oil price escalation. Unlike the oil price, oil price volatility comparably decreases GHG emissions in oil-exporting and -importing economies. Thus, one might be tempted to take oil price volatility and the future uncertainty of oil price as a virtuous instance rather than oil price increment. Thus, policymakers need to pay attention to market forces and policy measures to monitor GHG emissions due to economic activities. The results are also robust under the alternative econometrics estimation model of generalized method of moments (GMM)-Differenced.

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Section: Literature Reviewmentioning

confidence: 99%

“…Similarly, Esseghir and Haouaoui Khouni [21] propose a strategy of energy-saving and promotion policies as a priority to ensure sustainable development. Nevertheless, decreasing GHG emission without compromising economic growth is a significant challenge for policymakers and scientists [16].…”

Section: Introductionmentioning

confidence: 99%

Global Oil Price and Innovation for Sustainability: The Impact of R&D Spending, Oil Price and Oil Price Volatility on GHG Emissions

et al. 2021

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“…However, collinearity emerges among the independent variables of a multi-factor learning curve model, mimicking the said limitation of the single-factor learning curve and resulting in imprecise coefficient values in the model [45]. This issue was addressed in the two-factor learning curve model by introducing a residual variable [46], decreasing the VIF, and removing the correlation between the two factors. The four-factor learning curve model performed the worst as it was too complex for the given historical data and resulted in overfitting.…”

Section: Discussionmentioning

confidence: 99%

Projecting the Price of Lithium-Ion NMC Battery Packs Using a Multifactor Learning Curve Model

et al. 2020

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Renewable energy (RE) utilization is expected to increase in the coming years due to its decreasing costs and the mounting socio-political pressure to decarbonize the world’s energy systems. On the other hand, lithium-ion (Li-ion) batteries are on track to hit the target 100 USD/kWh price in the next decade due to economy of scale and manufacturing process improvements, evident in the rise in Li-ion gigafactories. The forecast of RE and Li-ion technology costs is important for planning RE integration into existing energy systems. Previous cost predictions on Li-ion batteries were conducted using conventional learning curve models based on a single factor, such as either installed capacity or innovation activity. A two-stage learning curve model was recently investigated wherein mineral costs were taken as a factor for material cost to set the floor price, and material cost was a major factor for the battery pack price. However, these models resulted in the overestimation of future prices. In this work, the future prices of Li-ion nickel manganese cobalt oxide (NMC) battery packs - a battery chemistry of choice in the electric vehicle and stationary grid storage markets - were projected up to year 2025 using multi-factor learning curve models. Among the generated models, the two-factor learning curve model has the most realistic and statistically sound results having learning rates of 21.18% for battery demand and 3.0% for innovation. By year 2024, the projected price would fall below the 100 USD/kWh industry benchmark battery pack price, consistent with most market research predictions. Techno-economic case studies on the microgrid applications of the forecasted prices of Li-ion NMC batteries were conducted. Results showed that the decrease in future prices of Li-ion NMC batteries would make 2020 and 2023 the best years to start investing in an optimum (solar photovoltaic + wind + diesel generator + Li-ion NMC) and 100% RE (solar photovoltaic + wind + Li-ion NMC) off-grid energy system, respectively. A hybrid grid-tied (solar photovoltaic + grid + Li-ion NMC) configuration is the best grid-tied energy system under the current net metering policy, with 2020 being the best year to deploy the investment.

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“…However, before explaining the steps involved in modified PC algorithms, it is important to discuss the assumptions and mathematical notation of modified PC algorithm. (Fazal et al, 2021) illustrates the following five steps.…”

Section: Condition Of Faithfulnessmentioning

confidence: 99%

“…Granger-type causality check causal ordering between two variables or nodes. However, there may also be a chance of third node -Z which is the common cause of both A and B (Fazal, Rehman, Rehman, Bhatti, & Hussain, 2021). (Demiralp, Hoover, & Perez, 2008) argues that Granger causality fails to capture structural causality.…”

Section: Introductionmentioning

confidence: 99%

Tracing Causality and Co-movement between Pakistani and the Leading Foreign Stock Markets: A Graph Theoretic Approach

Fazal¹,

Rehman²,

Alam³

2020

IRMBR

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This paper developed and modify Peter and Clark (PC) causality algorithm to revisit the causal linkages between Pakistan and the leading foreign stock markets. Initially, the PC algorithm was conceived to determine causality in cross sectional data. Later on, (Swanson & Granger, 1997) for the first time used VAR residuals in PC algorithm to determine the causal ordering in time series. However, the weak point attached to VAR residuals are that it carries only contemporaneous causal information and remove all the past information. This study modify the PC algorithm based on recursive residuals proposed by (Rehman & Malik, 2014) and explore the causality among exchange rate, interest rate and stock market prices. The overall empirical results of modified PC algorithm indicate that causality is running from exchange rate, interest rate and stock market of India and Bangladesh to Pakistani stock market. The results observed from GARCH-GJR model show spill over effect from the leading foreign stock markets toward Pakistan stock market excluding Sir Lanka. The results of the study will guide the investors to be vigilant in decision making in diversified portfolio investment and hedging. Keywords: Financial Markets, PC algorithms, Causality, Graph theoretic Approach, GARCH, GJR.

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Energy-environment-economy causal nexus in Pakistan: A graph theoretic approach

Cited by 22 publications

References 58 publications

Global Oil Price and Innovation for Sustainability: The Impact of R&D Spending, Oil Price and Oil Price Volatility on GHG Emissions

Global Oil Price and Innovation for Sustainability: The Impact of R&D Spending, Oil Price and Oil Price Volatility on GHG Emissions

Projecting the Price of Lithium-Ion NMC Battery Packs Using a Multifactor Learning Curve Model

Tracing Causality and Co-movement between Pakistani and the Leading Foreign Stock Markets: A Graph Theoretic Approach

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