Umer Shahzad
58959539400
Publications - 12
Exploring the nexus among green finance, renewable energy and environmental sustainability: Evidence from OECD economies
Publication Name: Renewable Energy
Publication Date: 2025-05-01
Volume: 244
Issue: Unknown
Page Range: Unknown
Description:
UN Sustainable Development Goals 13 and 7 on climate change mitigation and clean and responsible energy use serve as the driving forces behind this study. In the light of growing global concern, the current study seeks to evaluate the effect of renewable energy, green finance and institutional quality on carbon dioxide (CO2 ) emissions in the “Organization for Economic Co-operation and Development” (OECD) members from 2000 to 2022. To ascertain the impact of the relationship between these variables, the study employed the panel quantile autoregressive distributed lag (PQARDL). The cointegration test supports the validity of the long-term link between variables of the study. Apart from that, the estimated results have supported an inverted U-shaped link between CO2 and renewable energy over the long term in the median base (0.50) quantile group. These findings support the global sustainability agenda by illuminating the potential impact of robust institutional frameworks, renewable energy and sustainable finance practices on CO2 reductions. It also recommends that in order to achieve environmental sustainability and enhance environmental quality by lowering CO2 , OECD policy maker should prioritize the use of renewable energy sources and high-quality institutions.
Open Access: Yes
Exploring the impact of China's low carbon energy technology trade on alleviating energy poverty in Belt and Road Initiative countries
Publication Name: Energy
Publication Date: 2025-03-01
Volume: 318
Issue: Unknown
Page Range: Unknown
Description:
The objective of this study is to analyze how low-carbon technology imports such as wind turbines, solar panels, carbon capture equipment, and biomass systems from China affect Belt and Road Initiative (BRI) countries’ energy poverty. Additionally, we analyze the role of financial development, deliberative democracy, economic complexity, human development, and telecommunications infrastructure on energy poverty in BRI countries. We use 69 countries from Belt and Road initiative countries and a sample period from 2000 to 2019. We classify these countries according to the IMF classification of advanced, emerging and low-income developing countries. We employ the instrumental variable generalized method of moments (IV-GMM) approach as the main technique to take care of the endogeneity concerns inherent in the model, as well as a robust quantile-based technique called the method of moments quantile regression estimator (MMQREG). Our results reveal that low-carbon technology trade from China does not significantly alleviate energy poverty in the BRI countries. Financial development increases energy poverty while deliberative democracy decreases it. Economic complexity, as well as human development, negatively affects energy poverty, while telecommunications infrastructure does not affect energy poverty significantly. Based on the results, policy implications are provided.
Open Access: Yes
Impact of technological advancement and greener energy on sustainable agriculture in Asia: Evidence from selected Asian countries
Publication Name: Sustainable Development
Publication Date: 2025-02-01
Volume: 33
Issue: 1
Page Range: 221-237
Description:
Regardless of major advancements in food production, Asia continues to confront severe food security challenges. Sustainable agriculture presents entirely new prospects by prioritizing the productive worth of human, social, and natural capital—all of which are abundant in Asian nations or can be replenished at a relatively low financial expense. This paper sets out to explore the role of technological innovation, renewable energy use, financial development, globalization, and institutional quality on the environmental sustainability of agriculture, measured by the greenhouse gas emissions from the agricultural sector for top 10 agricultural economies of Asia from 1990 to 2019. To attain the above objective, we employ a variety of econometric models capable of accounting for cross-sectional dependence, including the CS-ARDL model and the Dumitrescu-Hurlin Panel Granger Causality tests. The result indicates that technological innovation as well as the use of renewable energy can reduce the greenhouse gas emissions from the agricultural sector and thus contribute towards enhancing environmental performance of this sector in short and long run. Although globalization result is revealed to be positive, it turns out to be insignificant in both short and long run. Financial development exerts positive and significant effects on agricultural emissions while the institutional quality is found to be increasing the agricultural environmental performance. Finally, we provide policy recommendations based on the results of the study.
Open Access: Yes
DOI: 10.1002/sd.3106
Enhancing cognitive metrics in supply chain management through information and knowledge exchange
Publication Name: International Journal of Logistics Management
Publication Date: 2025-01-01
Volume: 36
Issue: 7
Page Range: 200-221
Description:
Purpose: This research primarily aims to investigate the impact of organizational implants on knowledge transmission, process innovation and security integration in intricate supply chains. Design/methodology/approach: The research utilizes a mixed-method approach, employing a stratified sampling strategy to get a representative sample of 1,284 enterprises from various sectors within the logistics industry within the European Union. Data were gathered by computer-assisted web interviewing (CAWI) and analysed utilizing structural equation modelling (SEM) to evaluate hypotheses concerning cognitive congruence, process diffusion and security integration. Findings: The results indicate that while task interdependence clearly improves face-to-face communication, excessive cognitive congruence can hinder process innovation, resulting in what the article terms “cognitive rigidity.” The study suggests that achieving a balance between cognitive congruence and cognitive flexibility is crucial to improving the safety diffusion and integration process. Originality/value: This study presents an innovative conceptual framework that synthesizes cognitive congruence, cognitive flexibility and cognitive rigidity to examine their combined influence on knowledge transfer and process dissemination throughout supply chains. It presents cognitive stiffness as a boundary condition, contesting the conventional belief that more cognitive congruence is invariably advantageous.
Open Access: Yes
Pathways to sustainability: Evaluating the impact of green energy, natural resources, FinTech, and environmental policies in resource-abundant countries
Publication Name: Resources Policy
Publication Date: 2024-10-01
Volume: 97
Issue: Unknown
Page Range: Unknown
Description:
The escalating concerns about ecological sustainability have made the consumption of resources a crucial global issue. The speedy growth of the economy is heavily reliant on excessive consumption of resources, which significantly contributes to the imbalance between biodiversity and ecological footprint, resulting in a decrease in the carrying capacity. Both researchers and policymakers strive to enhance the amount of financial capital in the present time while ensuring that the country's economic growth remains unaffected. The primary objective of this study is to analyze the impact of green energy, financial technology (FinTech), and environmental regulations on enhancing the environmental sustainability of resource-rich countries from 1992 to 2022. To address problems with cross-sectional dependency and slope heterogeneity, this study employs the CS- ARDL model. The long-term results indicate that the reliance on income from natural resources decreased the load capacity factor. However, the load capacity factor was improved by shifting to green energy, adopting fintech, and implementing environmental regulations. The utilization of the AMG and CCEMG estimate procedures enhances the validity of the research findings. These findings provide essential policy recommendations for all stakeholder involved.
Open Access: Yes
Interplay between economic progress, carbon emissions and energy prices on green energy adoption: Evidence from USA and Germany in context of sustainability
Publication Name: Renewable Energy
Publication Date: 2024-10-01
Volume: 232
Issue: Unknown
Page Range: Unknown
Description:
In contemporary times, where most academic research mainly focuses on the factors of economic and environmental sustainability and emissions reduction. Yet, very little attention has been paid to the identification of the factors of renewable energy, which requires appropriate policy-level attention. Consequently, this research investigated two developed economies, i.e., Germany and the USA, from 1991 to 2021 where, the objective of the study includes using novel and robust empirical methods to test the causal relationship between renewable energy and CO2 emissions, economic growth, technological innovations and oil prices. Using the normality and unit root estimators, this study observed that non-normal data distribution, yet all the variables are stationary. Using time-series and panel cointegration tests, the results validate the cointegration between economic growth, oil prices, carbon emission, technological innovation, and renewable energy adoption in the United States whereas Germany does not show cointegration between the variables. This study employ ‘s the Morlet-Wavelet approaches and key findings show that all these variables have a significant role in improving renewable energy adoption in both the region. Furthermore, results show a unidirectional and bidirectional causal association between the variables via the panel-stacked Granger causality test. This study recommends effective policy ramifications concerning improved investment in technological innovation, improved low-carbon production, and diverting economic growth to renewable energy transition. Use of improved new time-series method of wavelet coherence show the key contribution in this paper with new evidence of time frequency analysis on how external variables affect renewable energy consumption in developed countries of US and Germany. The objective includes understanding the effects of CO2 emissions, economic growth, technological innovations and oil prices on renewable energy which would give evidence to policy makers and environmentalists on how developed countries should improve clean energy adoption.
Open Access: Yes
Resource savings, recycling and utilization, and energy transition: Introduction
Publication Name: Geoscience Frontiers
Publication Date: 2024-05-01
Volume: 15
Issue: 3
Page Range: Unknown
Description:
No description provided
Open Access: Yes
Asymmetric nexus between renewable energy, economic progress, and ecological issues: Testing the LCC hypothesis in the context of sustainability perspective
Publication Name: Gondwana Research
Publication Date: 2024-05-01
Volume: 129
Issue: Unknown
Page Range: 465-475
Description:
This paper examines the load capacity curve hypothesis by the tourism and renewable energy from top tourism economies in the World. We employ the data from 2000 to 2020 and applied the panel GMM and panel quantile regression to arrive at our empirical findings. The results of the two models demonstrate the non-validity of the Load Capability Curve (LCC) hypothesis and the significant role of touristic arrival (TRA) and greener energy consumption (GEC) on the load capacity factor (LCP) by contrasting the ecological footprint per capita and bio-capacity. Furthermore, renewable and clean energy is recommended to address air pollution and climatic vulnerability. Thus, the empirical results of the current study provide acumens for policymakers of top tourism economies to consume green innovation technologies to counterbalance the environmental and socio-economic issues induced by the tourism sector without halting economic growth and sustainable tourism development. The study discusses policy-related implications for sustainable development.
Open Access: Yes
Forecasting Bitcoin prices using artificial intelligence: Combination of ML, SARIMA, and Facebook Prophet models
Publication Name: Technological Forecasting and Social Change
Publication Date: 2024-01-01
Volume: 198
Issue: Unknown
Page Range: Unknown
Description:
In recent years, investors, corporations, and enterprises have shown great interest in the Bitcoin network; thus, promoting its products and services is crucial. This study utilizes an empirical analysis for financial time series and machine learning to perform prediction of bitcoin price and Garman-Klass (GK) volatility using Long Short-Term Memory (LSTM), Seasonal Autoregressive Integrated Moving Average (SARIMA), and Facebook prophet models. The performance findings show that the LTSM boost has a noticeable improvement compared to SARIMA and Facebook Prophet in terms of MSE (Mean Squared Error) and MAE (Mean Average Error). Unlike Long Short-Term Memory (LSTM), a component of Deep Learning (DL), the finding explains why the bitcoin and its volatility forecasting difficulty has been partially met by traditional time series forecasting (SARIMA) and auto-machine-learning technique (Fb-Prophet). Furthermore, the finding confirmed that Bitcoin values are extremely seasonally volatile and random and are frequently influenced by external variables (or news) such as cryptocurrency laws, investments, or social media rumors. Additionally, results show a robust optimistic trend, and the days when most people commute are Monday and Saturday and an annual seasonality. The trend of the price and volatility of bitcoin using SARIMA and FB-Prophet is more predictable. The Fb-Prophet cannot easily fit within the Russian-Ukrainian conflict period, and in some COVID-19 periods, its performance will suffer during the turbulent era. Moreover, Garman-Klass (GK) forecasting seems more effective than the squared returns price measure, which has implications for investors and fund managers. The research presents innovative insights pertaining to forthcoming cryptocurrency regulations, stock market dynamics, and global resource allocation.
Open Access: Yes
Interoperability of the revolutionary blockchain architectures and Islamic and conventional technology markets: Case of Metaverse, HPB, and Bloknet
Publication Name: Quarterly Review of Economics and Finance
Publication Date: 2023-12-01
Volume: 92
Issue: Unknown
Page Range: 112-131
Description:
This study examines how Islamic and conventional technology stock indices interact with blockchain technology assets including Metaverse, High-Performance Blockchain, and Blocknet, throughout different market conditions and horizons. A three-pronged approach is employed, namely the quantile cross-spectral coherency approach, the time-varying parameter vector autoregressions (TVP-VAR), and the Causality in quantiles methodology. The quantile coherency results show that connectivity between new generation blockchains and conventional and Islamic markets varies. It was found that the connectedness of the network of variables rises in the medium run and reaches its peak in the long run as a result of the TVP-VAR approach. It is evident from the results that the application of sharia screening only impacts the relationship between the technology stock index and digital assets significantly, but does not affect the connectedness and causality between the two. Technology managers, policymakers, and blockchain designers are able to gain new insights from these research findings.
Open Access: Yes
The nexus between agricultural land use, urbanization, and greenhouse gas emissions: Novel implications from different stages of income levels
Publication Name: Atmospheric Pollution Research
Publication Date: 2023-09-01
Volume: 14
Issue: 9
Page Range: Unknown
Description:
The current study establishes theoretical and empirical linkages among urbanization, economic growth, land use, and greenhouse gas (GHG) emissions. The prime objective of this article is to draw novel conclusions and policies for the different income levels of countries regarding the urbanization and agriculture sector land on environmental pollution. Employing panel data of 50 countries for the period 1990 to 2019, this study uses the lasso regression and non-parametric regression panel data methods to investigate the impacts of land use (arable, permanent pastures, and cropland), urbanization growth, and economic progress on the pollution levels. After estimating a Lasso regression to find the best auto-regressive predictive specification, we used an auto-regressive partially linear regression where each of the drivers’ effects was modelled non-parametrically. The elasticity effect of the urban population on emissions is significantly positive and sizable. In addition, the effect distribution shows a non-negligible share of observations with an elasticity higher than one. Urban population growth is a serious threat to climate change, as it seems to increase sharply CO2 emissions (although with an elasticity pace smaller than one). The elasticity effect of GDP is significantly negative, which implies that the scale of production, by triggering efficiency, can have a positive effect on emissions reduction. The results argue that agglomeration negative effects put in place by larger urban population can partly explain this finding. Overall, the study argues that urbanization growth and economic activities lead to GHG emissions, whereas the study also discusses novel implications and the role of agricultural land use apropos Sustainable Development Goals (SDGs). The empirical findings allow us to draw novel conclusions and guidelines in line with SDGs. The agricultural reforms might include irrigation and farming techniques such as spin farming, solar tube wells, tunnel farming, technology use agreements, plant double helix, etc.
Open Access: Yes
Economic Costs of Work Stoppages Caused by the COVID-19 Outbreak
Publication Name: Journal of the Knowledge Economy
Publication Date: 2023-01-01
Volume: Unknown
Issue: Unknown
Page Range: Unknown
Description:
This study explores the economic costs of work stoppages resulting from the COVID-19 pandemic. It utilizes a global multiregional dynamic computable general equilibrium model and finds that the higher the proportion of labor compensation in the initial factor distribution, the greater the economic damage. Macroeconomic loss was characterized by a monotonically increasing function, with developed countries potentially incurring greater losses than developing countries. The COVID-19 pandemic had significant negative impacts on the global labor market, with a decline in labor productivity; the cumulative global economic loss in 2020–2022 surpassed $10.4 trillion, of which the EU, the USA, and China contributed 30.44%, 18.74%, and 15.44%, respectively. Countries’ anti-epidemic responses showed great heterogeneity, with South Korea and China’s actions showing the dual advantages of protecting the economy and lives, whereas the EU failed to protect either lives or the economy. This article argues that it was necessary to adopt strict quarantine measures to control the spread of the virus in the early stages of the epidemic, but with a drop in the case fatality rate and the introduction of vaccinations, strict control measures had to be removed to protect the economy.
Open Access: Yes
