Addressing climate change impact on the energy system: a technoeconomic and environmental approach to decarbonisation

Assessing concurrent effects of climate change on hydropower supply, electricity demand, and greenhouse gas emissions in the Upper Yangtze River Basin of China

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.

It analyses the impact of global climate change on electricity demand and its respective economic cost in buildings covering an area of 1 km by 1 km in the city of Madrid. In order to know the energy demand, meteorological information has been produced with a spatial resolution of 50 meters, taking into account the three-dimensional structure of the buildings and the land use properties around the buildings. Climate variables are dynamically downscaled from 1° to 50 m using a nesting approach. Energy simulations of buildings are implemented with the EnergyPlus model. To determine the cost of impacts, the future distribution of energy sources in the two climate scenarios analysed and the corresponding 2012 prices of the Spanish Energy Commission are taken into account. Impacts on the area’s energy demand are calculated for 2030, 2050 and 2100 versus 2011 under two IPCC global climate projections: RCP 4.5 (emission stabilization scenario) and RCP 8.5 (little effort to reduce emissions). The expected changes in electricity consumption in the year 2100 are very important. RCP 8.5 shows a strong increase in electricity demand for cooling buildings. In RCP 4.5 decreases in electricity consumption are observed (-14.37%) due to very important decreases in temperature. On average, the global climate for the year 2100 will have an impact on a typical building block in Madrid of 117918 euros per year according to scenario RCP 8.5 while in scenario RCP 4.5 110537 euros per year would be saved.

The conventional time series methods tend to explore the modeling process and statistics tests to find the best model. On the other hand, machine learning methods are concerned with finding it based on the highest performance in the testing data. This research proposes a mixture approach in the development of the ARDL (Autoregressive Distributed Lags) model to predict the Cayenne peppers price. Multiple time series data are formed into a matrix of input-output pairs with various lag numbers of 3, 5, and 7. The dataset is normalized with the Min-max and Z score transformations. The ARDL predictor variables of each lag number and dataset combinations are selected using the forward selection method with a majority vote of four criteria namely the Cp (Cp Mallow), AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion), and adjusted R2 . Each ARDL model is evaluated in the testing data with performance metrics of the RMSE (Root Mean Square Error), MAE (Mean Absolute Error), and R2 . Both AIC and adjusted R2 always form the majority vote in the determining optimal predictor variable of ARDL models in all scenarios. The ARDL predictor variables in each lag number are different but they are the same in the different dataset scenarios. The price of Cayenne pepper yesterday is the predictor variable with the most contribution in all of the 9 ARDL models yielded. The ARDL lag 3 with the original dataset outperforms in the RMSE and MAE metrics while the ARDL lag 3 with the Z score dataset outperforms in the R2 metric.

PurposeThis study aims to examine the impacts of climate change (CC), measured average annual rainfall, average annual temperature and carbon dioxide (CO2e) on cereal production (CPD) in Bangladesh by using the annual dataset from 1988–2014, with the incorporation of cereal cropped area (CCA), financial development (FD), energy consumption (EC) and rural labor force as important determinants of CPD.Design/methodology/approachThis study used an auto-regressive distributive lag (ARDL) model and several econometric approaches to validate the long- and short-term cointegration and the causality directions, respectively, of the scrutinized variables.FindingsResults of the bounds testing approach confirmed the stable long-term connections among the underlying variables. The estimates of the ARDL model indicated that rainfall improves CPD in the short-and long-term. However, CO2e has a significantly negative impact on CPD both in the short-and long-term. Results further showed that temperature has an adverse effect on CPD in the short-term. Among other determinants, CCA, FD and EC have significantly positive impacts on CPD in both cases. The outcomes of Granger causality indicated that a significant two-way causal association is running from all variables to CPD except temperature and rainfall. The connection between CPD and temperature is unidirectional, showing that CPD is influenced by temperature. All other variables also have a valid and significant causal link among each other. Additionally, the findings of variance decomposition suggest that results are robust, and all these factors have a significant influence on CPD in Bangladesh.Research limitations/implicationsThese findings have important policy implications for Bangladesh and other developing countries. For instance, introduce improved cereal crop varieties, increase CCA and familiarizes agricultural credits through formal institutions on relaxed conditions and on low-interest rates could reduce the CPD’s vulnerability to climate shocks.Originality/valueTo the best of the authors’ knowledge, this study is the first attempt to examine the short- and long-term impacts of CC on CPD in Bangladesh over 1988–2014. The authors used various econometrics techniques, including the ARDL approach, the Granger causality test based on the vector error correction model framework and the variance decomposition method.

The demand for electricity is soaring, propelled not only by population and GDP growth but also the pressing effects of climate change. This study seeks to address the uncertainties surrounding future electricity demand by projecting monthly consumption in Florida, USA, taking into account diverse climate scenarios and their potential impacts. Our approach involves utilizing the degree-day method and constructing an energy consumption regression model grounded in historical data. Key variables, including population, employment, GDP, electricity prices, temperature, and daylight hours, are systematically analyzed. This model acts as the fundamental basis for forecasting future electricity needs in residential, commercial, and industrial sectors across the state of Florida up to the year 2050, considering different climate scenarios. Under the Representative Concentration Pathway (RCP) 4.5 scenario, the residential sector foresees a substantial 63% increase in electricity demand from 2001–2019 to 2050. Under the more extreme RCP 8.5 scenario, this surge climbs to 65%. Meanwhile, the commercial and industrial sectors are expected to witness a 47% and 54% upswing in demand under RCP 4.5 and RCP 8.5, respectively. Intriguingly, heightened demand for cooling during scorching summers outweighs the reduced need for heating in winter, particularly in the residential sector. The current renewable energy policies fall short of addressing the impending climate-driven surge in electricity demand. To combat this, our recommendation is the implementation of a Renewable Portfolio Standard, aimed at significantly enhancing the proportion of renewables in Florida's electricity mix. This paper concludes with a set of crucial policy recommendations, imperative for steering a sustainable transition to renewable energy and effectively managing the impacts of extreme heat on people's lives. These recommendations serve as a strategic roadmap for navigating the evolving landscape of electricity demand amidst the complex challenges posed by climate change.

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.

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.

Regional and temporal variations in the impacts of future climate change on Japanese electricity demand: Simultaneous interactions among multiple factors considered

Objective: The objective of this study is to investigate the evolution of date production in Algeria and its impact on the country's national income (GDP) from 2000 to 2019. This research aims to analyze the short-term and long-term relationships between date production and national income, assessing the significance of agricultural policies in enhancing the productivity and economic contribution of the date sector. Ultimately, the study seeks to highlight the role of date production in diversifying Algeria's exports and contributing to sustainable economic growth. Theoretical Framework: This study explores the relationship between date production and national income in Algeria, using agricultural productivity theory, endogenous growth theory, export diversification theory, and the ARDL model. It aims to understand the impact of agricultural policies, human capital investment, and export diversification on Algeria's date production, highlighting the potential for economic diversification. Method: This study analyzes the relationship between date production and Algeria's GDP from 2000 to 2019, using the Autoregressive Distributed Lag (ARDL) model. Data sources include Algeria's GDP and date production data. The study uses unit root tests, bounds testing, and error correction models to estimate the relationship. Statistical software like EViews or R is used for analysis. Results and Discussion: The study reveals a positive relationship between date production and national income in Algeria from 2000 to 2019. The data suggests that agricultural policies have led to an upward trajectory in date production, while GDP has grown significantly. The study also highlights the importance of date production in Algeria's economy, particularly in diversifying exports. The long-term relationship suggests sustained investment in date production will yield continued benefits for national income. Research Implications: The study highlights the importance of supporting agricultural policies that enhance productivity in Algeria's date sector. It suggests broader export diversification strategies, infrastructure development, and economic development. Strengthening date production can create job opportunities, stimulate rural development, and contribute to food security. Future research should explore other agricultural products, climate change, and technological innovations to improve yields and sustainability in the sector. Originality/Value: The study explores Algeria's date production and national income relationship, providing insights for policymakers to diversify the economy beyond hydrocarbons and foster sustainable growth.

Indonesia is a maritime country that has a high production of fish. West Java is one of the provinces which accounted for a high fish production in Indonesia with total production is 8,316,607.377 tons in 2011. The fish production in West Java has a trend and seasonal components. The trend and seasonal components is influenced by climate change. One of interesting indicator of the climate change is the change in intensity of rainfall. The increasing intensity of rainfall would be increase fish production. The influence rainfall on fishing production can be formulated in a mathematical model using Autoregressive Distributed Lag Modeling (ARDL). This method was applied because of the impact of the lag of independent variables and dependent variable that included in the model. The model informs how big the impact. The parameter estimation was conducted using ordinary least squares (OLS) and obtained adjusted R2 = 0.8265. The high fish productions in the previous period affect the decline in fish production in the next period.

The objective of this study is to examine the short‐run and long‐run impact of macroeconomic variables on E7 stock indices across bullish, bearish and normal states of the stock markets. For this purpose, this study uses both autoregressive distributed lag (ARDL) and quantile ARDL (QARDL) models. The findings based on the ARDL model indicate that, in the long‐run, foreign direct investment (FDI), trade balance and industrial production index (IPI) significantly affect emerging stock indices. In addition, the findings based on the QARDL model indicate that the short‐run effect of FDI, consumer price index, interest rate and exchange rate varies across bullish, bearish and normal states of the emerging stock markets, whereas the long‐run effect varies for all macroeconomic variables except IPI. These findings indicate that the results change when QARDL model is used; however, these findings remain same across seven emerging stock indices. Finally, this study proposes important policy recommendations based on the findings of this study.

Evaluating the joint effects of climate and land use change on runoff and pollutant loading in a rapidly developing watershed

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.

PurposeThis study aims to examine the symmetric and asymmetric impact of external debt on inflation in Sudan from 1970 to 2020 within a multivariate framework by including money supply and the nominal effective exchange rate as additional inflation determinants.Design/methodology/approachThe authors utilize an Auto Regressive Distributed Lag (ARDL) model to examine the symmetric impact of external debt on inflation, while the asymmetric impact is examined using a Nonlinear ARDL (NARDL) model. The existence of a long-run relationship between inflation and external debt is tested using the bounds-testing approach to cointegration, and a vector error-correction model is estimated to determine the short parameters of equilibrium dynamics.FindingsThe linear ARDL model results show that external debt has no statistically significant impact on inflation in the long run. On the contrary, the results of the NARDL model show that positive and negative external debt shocks statistically affect inflation in the long run. The estimated long-run elasticity coefficients of the linear and nonlinear ARDL models reveal that the domestic money supply has a statistically significant positive impact on inflation. In contrast, the nominal effective exchange rate has a statistically significant negative impact on inflation.Practical implicationsThe reliance on symmetric analysis may not be sufficient to uncover the existence of a linkage between external debt and inflation. Proper external debt management is crucial to control inflation rates in Sudan.Originality/valueTo date, no empirical study has assessed the external debt-inflation nexus and its potential asymmetry in Sudan, and the current study aims to fill this gap in the literature.