Addressing the impact of population pressure on carbon dioxide emissions: an empirical investigation among the interplay of output, population, and carbon emission

This study examines the dynamic relationships among output, carbon emission, and population of China during the period of 1960 to 2009 by using auto regressive distributive lag (ARDL) model. In order to make the co-integration analysis robust, the paper applied Toda and Yamamoto causality test to understand the causal links among output, carbon emission, and population. The empirical results suggest that population significantly affect output whereas carbon emission does not affect output in the long run. The error correction model (ECM) version of ARDL model found deviations from long run equilibrium which is due to dynamics in the variables concerned. Toda and Yamamoto causality test revealed bi-directional causality between population and carbon emission and also between population and output. Empirical results signify that population can be considered as a powerful ingredient in the context of increase in carbon emission which is instrumental to environmental conservation in China.

Purpose: This study was conducted to investigate the socioeconomic determinants of tuberculosis (TB) in Nigeria. The prevalence of TB in Nigeria in recent years has been on thunderous increase, and this has led to poor health outcome and dwindled economic growth. Nigeria government has put different measures to stop the prevalence of TB in Nigeria, but it seems their efforts are fruitless. This situation becomes a great challenge to the people and the government. These facts motivated this study to empirically investigate socioeconomic factors/determinants which may have been related to TB continuous prevalence despite the government efforts to stop its menace in Nigeria. Design/methodology/approach: This study used auto regressive distributed lag (ARDL) model for its design and methodology. Unit root test was conducted at the initial stage which led to the decision of using the ARDL model. The ARDL bound test, coefficient test, error correction model, and diagnostic test were conducted. The data used in this study is annual secondary data ranging from 1985 to 2018. The data were sourced from a reliable means. Findings: This study finding shows that there are socioeconomic determinants/factors which related to TB and can control the prevalence of TB in Nigeria. Socioeconomic determinants like income, education, savings, and final consumption expenditure (FCE) were used in this study and they showed a positive relationship with TB. It was only savings and FCE that were significant at 5% and 10%, respectively proving that increase in savings and FCE leads to increase in TB prevalence in Nigeria, which simply implies that people should stop savings in order to fight, control and reduce TB prevalence. Secondly, when FCE is increased meaning no money left to spend to curtail TB, then TB prevalence will increase. Income and education were not significant with TB because savings and FCE are components of income, and they were used in the study. Increase in education may lead to increase in TB prevalence because of the nature of TB transmission from one person, one place to another.

Background: The provision of energy services is a vital component of the energy system. This is often considered emission-intensive and at same time, highly vulnerable to climate change conditions. This forms the fundamental objective of this thesis, poised to examine technoeconomic and environmental implications of policy intervention, targeted at cushioning impacts of climate change on the energy system. Aims: Four research queries are central to this work: (1) Review literature on impacts of CVC (2) Estimate influence of seasonal climatic and socioeconomic factors on energy demand in Australia; (3) Model dynamic interactions between energy policies and climate variability and change (CVC and (4) Identify least-cost combination of electricity generation technologies and effective emissions reduction policies under climate change conditions in Australia. Methods: A systematic scoping review method was first applied to identify consistent pattern of CV&C impacts on the energy system, while spotting research gaps in studies that met the inclusion criteria. Databases consisting of Scopus and Web of Science were searched, and snowballing references in published studies was adopted. Data was collated and summarised to identify the characteristic features of the studies, consistent pattern of CV&C impacts, and locate research gaps to be filled by this study. The second study applied an autoregressive distributed lag (ARDL) model to estimate temperature sensitive electricity demand in Australia. Estimates were used with projected temperatures from global climate models (GCMs) to simulate future electricity demand under climate change scenarios. The study further accounted for uncertainties in electricity demand forecasting under climate change conditions, in relation to energy efficiency improvement, renewable energy adoption and electricity price volatility. The estimates from the ARDL model and projections from GCMs were used for energy system simulation using the Long-range Energy Alternative and Planning (LEAP) system. It considered climate induced energy demand in the residential and commercial sector, alongside linking the non-climate sensitive sector with energy supply sector. This model was vital to justifying policy options under investigation. Further, LEAP modelling analysis was extended by identifying effective emission reduction policies considering CV&C impacts. Here, the Open Source Energy Modelling System (OSeMOSYS) was used for optimisation analysis to identify least-cost combination of electricity generation technologies and GHG emission reduction policies. Whereas, in the third and final study, cost-benefit analysis and estimation of long run marginal cost of electricity were conducted, while decomposition analysis of GHGs were analysed in the third study alone. Data used in the ARDL model included socioeconomic data which includes gross state product, as well as population and electricity prices from 1990-2016. The LEAP and OSeMOSYS model as used, was dated to 2014 as the base year, while several technological (power plant characteristics, household technologies), economic (energy prices, economic growth, carbon price) and environmental (emission factors, emission reduction target) variables were used to develop Australia's energy model. Results: The literature search generated 5,062 articles in which 176 studies met the inclusion criteria for the final literature review. Australian studies were scarce compared to other developed countries. Also, just few articles made attempt to examine decarbonisation under climate change. The ARDL model estimates and GCMs simulation of future electricity demand under CV&C show that Australia had an upward sloping climate-response functions, resulting to an increase in electricity demand. However, the researcher identified an annual increase in projected electricity demand for states and territory in Australia, which calls for the need to scale up RET. The LEAP model results showed substantial impacts on energy demand, as well as impacts on power sector efficiency. Under the BAU scenario, CV&C will result in an increase in energy demand by 72 PJ and 150 PJ in the residential and commercial sectors, respectively. Induced temperature enlarges the non-climate BAU demand, which will increase threefold before 2050. Under the non-climate BAU, there is an expansion of installed capacity to 81.8 GW generating 524.6 TWh. Due to CV&C impacts, power output declines by 59 TWh and 157 TWh in Representative Concentration Pathways (RCP) 4.5 and 8.5 climate scenarios. This leads to an increase in generation costs by 10% from the base year, but a decrease in sales revenue by 8% and 21% in RCP 4.5 and RCP 8.5, respectively. The LEAP-OSeMOSYS model suggests renewables and battery storage systems as least-cost option. However, the configuration varied across Australia. Carbon tax policy was observed to be effective in reducing Australia's emission and foster huge economic benefits when compared to the current emission reduction target policy in the country. Also, renewable energy technologies increase electricity sales and decrease fuel cost better than fossil fuel dominated scenarios. Conclusions: Data from this study reveals that seasonal electricity demand in Australia will be influenced by warmer temperatures. Also, the study identified the possibility of winter peaking which is somewhat higher than summer peak demand in some states located in the southern regions of Australia. However, winter peaking is projected to decline by mid-century across the RCPs, while summer peak load is projected to increase, thereby, causing power companies to expand their generation capacity which may become underutilised. Owing to increase in cooling requirements up to 2050, policy uncertainties analysis recommend renewables to match an increasing future electricity demand. The energy model indicates that ignoring the influence of CV&C may result in severe economic implications which range from increased demand, higher fuel cost, loss in revenue from decreased power output, as well as increased environmental externalities. The study concludes that policy options to reduce energy demand and GHG emissions under climate change may be expensive on the short-run, though, may likely secure long-run benefits in cost savings and emission reductions. It is envisaged that this could provide power sector management with initiatives that could be used to overcome cost ineffectiveness of short-term cost. The modelling results makes a case for renewable energy in Australia as lower demand for energy and increased electricity generation from renewable energy source presents a win-win case for Australia.

This study examines the impact of fossil fuel consumption, nonrenewable energy consumption, population, affluence, and poverty on carbon emissions in Pakistan by using a time series data from 1972 to 2014. The study uses a flexible ecological framework known as the STIRPAT model. The Auto Regressive Distributive Lag (ARDL) Model and Error Correction Model (ECM) are used to estimate the robust results. The results show that consumption of fossil fuels, population growth, improvement in affluence level, and urbanization are contributing factors to high carbon emissions in Pakistan. The results also highlight that poverty alleviation and carbon emissions have opposite trends, this shows that the efforts to reduce poverty are stimulating the consumption of low-cost energy sources such as fossil fuels, and contributing to carbon emissions. However, results indicate that an increase in the share of renewable energy in total energy use and consumption of hydroelectric energy has the potential to reduce carbon emissions in Pakistan. The results highlight that there is a need to promote the use of renewable and hydroelectric energy. At domestic level, this will assist to meet the energy demand of the growing population and also prove helpful to reduce carbon emissions. Thus, the study recommends that a transition from fossil fuel energy to renewable and hydroelectric energy could prove an effective strategy to improve the affluence level, to alleviate poverty and effective to reduce carbon emissions in Pakistan.

Carbon dioxide (CO2) emission reduction has become an important concern worldwide. During the past century, human activities have been a significant cause of the increase in the level of greenhouse gases. Past research mainly focuses on evaluating the nexus between unidimensional population factors and CO2 emissions, while few prior studies in a developing country have reported the impact of multidimensional demographic factors on CO2 emissions. As an initial attempt, this study investigates the short- and long-run associations between population factors, low-carbon innovation, and carbon dioxide emissions (CO2) for a panel consisting of 285 cities by employing the pooled mean group (PMG) estimator under the framework of the panel autoregressive distributed lag (ARDL) model. Our main findings are as follows: (1) Population size and population density could increase CO2 emissions, while population quality and low-carbon innovation were essential factors that alleviate carbon emission pressure in the long run. (2) Economic development, foreign direct investment, and industrial development were found to be factors causing the increase in carbon emissions. (3) The split-sample analysis demonstrated that the improvement of population quality still has a positive and significant long-run effect on environmental quality. Simultaneously, low-carbon innovation could realize the enormous dividends of carbon emission reduction in the long run, especially in existing relatively larger CO2 emission areas. Finally, the paper presents important policy implications.

Thermal error modeling method is an important field of thermal error compensation on NC machine tools, it is also a key for improving the machining accuracy of machine tools. The accuracy of the model directly affects the quality of thermal error compensation. On the basis of multiple linear regression (MLR) model, this paper proposes an autoregressive distributed lag (ADL) model of thermal error and establishes an accurate ADL model by stepwise regression analysis. The ADL model of thermal error is established with measured data, it proved the ADL model is available and has a high accuracy on predicting thermal error by comparing with MLR models.

As the challenge of global climate change becomes increasingly severe, carbon emissions have become a key constraint on sustainable development. This study aims to explore the impact of economic growth, urbanization, and transportation infrastructure on carbon emissions in China. Using time-series data from 1977 to 2022, the study employs the Autoregressive Distributed Lag (ARDL) model to analyze the short-term and long-term dynamic relationships between these variables, and the Vector Error Correction Model (VECM) to assess the causal relationships. The ARDL regression results show that, in the short run, economic growth has an immediate significant positive effect on carbon emissions, while urbanization exhibits mixed lagged effects—initially increasing and later reducing emissions. Transportation infrastructure has no immediate impact but shows a significant emission-reducing effect through its lagged terms. In the long run, economic growth exhibits an insignificant negative impact on emissions, urbanization has an insignificant positive effect, and the expansion of transportation infrastructure is positively associated with increased carbon emissions. Granger causality analysis reveals that carbon emissions and urbanization exhibit a bidirectional causal relationship in the short run. In the long run, carbon emissions are mutually causal with economic growth, and are also unidirectionally influenced by transportation infrastructure. This study emphasizes the importance of developing an integrated policy framework to balance economic growth, urbanization, and transportation infrastructure with environmental sustainability.

Economic and financial theories postulate that stocks should provide a hedge against expected inflation. Since the work of Fisher (1930), there has been on-going empirical investigation into testing the relationships between stock price indices and consumer price indices, in levels and first differences. The findings of these investigations are mixed. One concern is that the methodological issues associated with these studies are not adequately addressed in the literature. The main contribution of this thesis is to identify and improve upon the weaknesses of some of the methodologies employed for testing this relationship and apply the improved methods to Australian data. This thesis conducts investigation into the short run and long run relationships between Australian stock and consumer price indices, in levels and first differences, using bivariate and multivariate frameworks. In addition, this thesis examines, whether or not, the major monetary policy change introduced by the Reserve Bank of Australia in January 1990, has had any significant influence on these relationships. During the period leading up to this change, Australia experienced a high inflationary environment. Using the quarterly data for the period 1969 to 2008 and employing vector autoregression (VAR), autoregressive distributed lag (ARDL) models and bootstrap methods, this thesis presents robust statistical inference on the relationships between stock and consumer price indices. A review of the literature suggests that previous empirical studies investigating this relationship paid inadequate attention to improving the statistical inference on the long run parameters. This thesis makes two major improvements to the methodologies used by previous empirical studies: one is the construction of bootstrap confidence intervals for VAR impulse responses. The other is the estimation of the long run model parameters that are nonlinear functions of those of ARDL models by employing bootstrap methods. Traditionally, OLS and delta methods are used to estimate these long run parameters, although the latter method is known to work well only with large samples under normality. Such strong requirements do not appear to be satisfied for the empirical models studied in the thesis. Here, a bias-corrected bootstrap method for estimating long run model parameters and their confidence intervals is adopted when the normality assumption is violated, and the wild bootstrap method is adopted when both normality and homoscedasticity assumptions are violated. Based on the VAR impulse response functions and bootstrap confidence intervals, this thesis finds that there is a short run negative relationship between stock returns and inflation. The long run ARDL model estimates indicate that the real stock returns are independent of expected inflation, suggesting that Australian stocks constitute a good hedge against expected inflation. Furthermore, the empirical results indicate that the relationship between stock returns and inflation is not affected by the major monetary policy change introduced in January 1990. No evidence of a long run relation between real stock prices and consumer prices was found for the more recent low inflationary period. Based on the empirical evidence presented in the thesis, the overall conclusion is that Australian stocks provide a hedge against inflation, from which domestic and foreign investors can benefit.

This study aims to approach the Fisher effect issue from a different methodological perspective. To this aim, the nonlinear autoregressive distributed lag (ARDL) model, recently developed by Shin et al. (2014), is applied for South Korea between 2000Q4Ƀ2017Q4. This model allows us to decompose one variable (changes in inflation) into two new variables (increases and decreases in inflation) under the manners of nonlinearity and asymmetry. Hence, it enables us to monitor the Fisher effect in terms of increases and decreases separately. We also apply the linear version of the same model since the nonlinear ARDL model is the extended version of linear ARDL model. While the empirical findings of the nonlinear model support asymmetrically partial Fisher effects in the long-run for 1, 3, 5 and 10-years Korean bond rates, the linear model does not. Additionally, the nonlinear model detects lower size partial Fisher effects when the maturity of interest rates gets longer. Another finding of this study is that the nonlinear model may mathematically identify and introduce a different version of the partial Fisher effect based on singular (separate) effects of each decomposed variable in a parametric manner.

PurposeThis study takes a disaggregated approach to investigate the impacts of long-run GDP on changes in total government expenditure in the Eastern Caribbean Currency Union (ECCU) economies. An understanding of the relationship between changes in total government expenditure and GDP (by sector categories) is expected to provide a working tool to understand the growth debt nexus of Caribbean countries. The purpose of the paper is to use an auto regressive distributed lag (ARDL) and error correction model (ECM) to examine and analyse short- and long-run dynamics of disaggregated approach to both output and government expenditure in a dynamic model to identify the growth in the Eastern Caribbean Countries.Design/methodology/approachIn an attempt to examine the long-run dynamics, data for the period 1970-2015 were used in an ARDL and ECM framework. The authors examine the long-run GDP impacts of changes in total government expenditure and in the shares of different spending categories for the ECCU countries to establish and analyse short and long-run dynamics.FindingsThe results suggest that total fiscal expenditure and disaggregated expenditure including debt services have both positively and negatively contributed to economic growth in the agriculture, manufacturing and mining sectors. Among others, the study found that high national debt in the region resulted primarily from increases in government expenses and diminishing income sources.Originality/valueThis paper is the first to take a disaggregated approach to investigate the relationship between economic growth and government expenditure in the Eastern Caribbean States. The authors’ empirical results suggest that debt servicing reduces economic growth both in the short and long run. The greatest impact being felt in the mining and manufacturing sectors, namely, 1 per cent increase in debt service will bring about 7.90 and 1.67 per cent decrease in economic growth. These results offer fairly strong support to the view that expenditure share variables can weaken sectoral growth, and hence force the overall growth to decline.

Climate change is a critical global issue, driven primarily by the continuous rise in carbon dioxide (CO2) levels. Addressing this challenge requires innovative solutions and proactive measures to mitigate its impact. This study investigates the impact of Bangladesh’s industrialization, agriculture, and imports on CO2 emissions, exploring both linear and asymmetric relationships to inform sustainable development strategies. Advanced modeling techniques, namely autoregressive distributed lag (ARDL) and nonlinear autoregressive distributed lag (NARDL) models are used to evaluate the impact of Bangladesh’s agricultural and industrial sectors on CO2 emissions. Time-series data ranging from 1990 to 2022 are analyzed to ensure data stationarity, employing the augmented Dickey-Fuller (ADF) test. Subsequently, the existence of non-linear associations is validated using the Brock-Dechert-Scheinkman (BDS) test, with further confirmation through bounds testing to establish both symmetric and asymmetric long-run cointegrating relationships. Long and short-run coefficients are assessed using linear and asymmetry ARDL models, revealing that industrialization contributes to increased carbon emissions in Bangladesh. While the ARDL model reports that the effect of agriculturalization on CO2 emissions is insignificant in the long-run, the asymmetry ARDL model suggests a rapid reduction in carbon emissions due to agriculturalization, observed both in the long and short-run. Additionally, imports have considerable impact on carbon emissions. Diagnostic tests have confirmed the adequacy of the model, while stability tests have validated the estimated parameters’ stability. Finally, the direction of association between variables is determined by applying linear and nonlinear Granger causality tests. This study underscores the importance of promoting sustainable industrial practices, enhancing agricultural efficiency, and regulating imports as pivotal strategies for mitigating CO2 emissions and achieving enduring environmental sustainability in Bangladesh.

Abstract. This study examines the long-term and short-term balance relationship of GDP, Foreign Direct Investment to the performance of nonoil exports in KSA within the framework of the export-led growth (ELG) hypothesis: Evidence from ARDL, VECM and a smaller evaluation according to Vision 2030. We performed an analysis for the period from 1970 to 2019 by an autoregressive distributed lag (ARDL) model and checked the robustness of the results in the vector error correction (VECM) model. The co-integration and Toda - Yamamoto causality analysis are conducted by using two techniques of vector error correction model (VECM) and autoregressive distributed lag (ARDL). The main findings are: Foreign direct investment can increase GDP growth rates by increasing non-oil exports in the Saudi economy according to the results of the Toda - Yamamoto Causality Test; and the GDP in the Saudi economy are affected by FDI and the rates of non-oil exports, in the long and short term, and the reason is the strength of the reserves of the Saudi economy. The contribution of this research is that the outcomes found by means of econometric models can be used for predicting and measuring GDP in upcoming years, can get a guideline from this research To the economic policy makers in Saudi Arabia. Also, the dynamic interaction among FDI, non-oil exports, and economic growth is investigated using the ARDL. The ARDL co-integration results showed that GDP, FDI and non-oil exports are co-integrated, indicating the presence of a long-run equilibrium relationship between them. Besides, the results for the relationship between GDP, FDI and Non-Oil Exports are interesting and indicate that there is no significant from variables and vice-versa using Toda-Yamamoto causality. Keywords. GDP, FDI, Non-oil exports, Stationary, Toda-Yamamoto Test, VECM, ARDL. JEL. D42, D43, H21, L12, L13.

This study evaluates and compares the forecasting performances of the ARDL (AutoRegressive Distributed Lag), LSTM (Long Short-Term Memory), and XGBOOST (Extreme Gradient Boosting) models on the MASI (Moroccan All Shares Index). The analysis incorporates daily new COVID-19 cases into the ARDL approach to investigate short-term and long-term relationships with MASI. Cointegration and causality tests are conducted on daily time series data. In terms of accuracy, the ARDL model, especially when including trend and seasonality variables, outperforms LSTM and XGBOOST models. The ARDL model with lags, trend, and seasonality variables achieves the lowest Mean Absolute Percentage Error (MAPE) of 26.7%, with a processing time of 1 second. In comparison, the LSTM and XGBOOST models have MAPE values of 30.5% and 32%, respectively, while requiring significantly longer processing times. These findings suggest that the ARDL model is more efficient and accurate in predicting future values of MASI under pandemic conditions.

Research on the dynamic relationship between China's renewable energy consumption and carbon emissions based on ARDL model

This article utilised the panel autoregressive distributed lag (ARDL) model to examine the link between macroeconomic developments and carbon emissions for 15 Asian countries from 1990 to 2013. The results of the panel ARDL model showed that there exist long-run equilibrium relationships between principal macroeconomic variables and carbon emissions in the sample. The long-run elasticity of renewable energy and fossil fuels energy consumption with respect to CO2 emissions was calculated as −0.299 and 0.967, respectively. The long-run elasticity of GDP, financial development, urban population density and industry value added share with respect to CO2 emissions was calculated as 0.473, 0.079, −0.633 and −0.10, respectively. FDI was significantly negatively related to CO2 emissions which were calculated as −0.06 in the short-run. These results suggested that FDI inflow was not yet an environmental threat for Asian economies. Renewable energy and upgrades to industry value added share will help various governments mitigate carbon emissions.