The role of biomass energy consumption and economic complexity on environmental sustainability in G7 economies

This paper empirically examines the effect of biomass energy consumption and economic complexity on environmental sustainability in G7 economies. The current study attempts to report a comprehensive analysis of biomass energy and economic complexity on ecological and carbon footprints and carbon emissions. We employ data from 1990 to 2019 and adopt robust panel econometric techniques that account for the analysis's cross-sectional dependence. We conduct cointegration analysis, pooled ordinary least squares (OLS), system generalized method of moments (GMM) and conditional quantile model for our empirical analysis. The empirical findings show that both biomass energy consumption and economic complexity are detrimental to the ecological footprint and carbon footprint. Additionally, we find that globalization positively affects the environment, while we find some evidence that bureaucratic quality improves environmental quality. Finally, in line with other research, we find that economic growth has detrimental effects on the environment. Our results suggest that policymakers should be more cautious in promoting biomass as a clean energy source and that the G7 economies should take advantage of their leading position in innovation to invest more in sustainable practices and investment.     

Foreign Direct Investments,Renewable Electricity Output,and Ecological Footprints: Do Financial Globalization Facilitate Renewable Energy Transition and Environmental Welfare in Bangladesh?

Asia-Pacific Financial Markets - Phasing out fossil fuel dependency to adopt renewable energy technologies is pertinent for both ensuring energy security and for safeguarding the well-being of the...     

The use of digital footprints in the US mortgage market

We study the rise of digital footprint (DF) users in the U.S. residential mortgage market. The proportion of lenders that use a borrower's DF has witnessed remarkable growth from 6% in 2013 to 34% in 2018 in a short span – according to our analysis. We show that the use of DFs can significantly reduce a lender's overall risk and that the use of DFs can result in considerable societal benefits, by reducing the overall discriminatory forces. In sum, we provide evidence that the informational advantage associated with using a borrower's DF far outweighs that of existing traditional lending methods.     

Bootstrap rolling-window Granger causality dynamics between momentum and sentiment: implications for investors

Annals of Finance - This paper seeks to examine the unidirectional versus bidirectional Granger causality between investors’ sentiment and momentum strategies. It is based on the full sample...     

Electricity Tariff Changes and Consumer Sentiment on Household Consumption Expenditure in Malaysia

Purpose

This study investigates the impact of the current electricity tariff, represented by optimized tariff calculation, on Malaysian household’s consumption across different levels of income.

Design/methodology/approach

The input-output (IO) model has vastly been used in many energy economics literature that presented the matrix of production between various sectors in an economy. This study aggregated the 124 subsectors in IO Price Table 2015 into 12 groups of consumption of goods and services based on Household Expenditure Survey 2019 (HES 2019) to meet the study’ objectives.

Findings

This study found that in all simulations, high-income earners would be highly affected by the tariff changes. The lower the increment level in electricity tariff rate, the lower the magnitude would be on the changes of household expenditure level.

Research limitations/implications

Optimization in electricity tariff consumptions can pattern the Malaysian household’s consumption across different levels of income efficiently.

Practical implications

Useful to all consumer in the Malaysia economic business sector to predict their energy consumption up to optimum level.

Social implications

The study’s findings can benefit the society in optimiza their electricity consumption since everyone requires the energy for basic needs in their life.

     

Cultural influences on future transportation technology usage: The role of personal innovativeness,technology anxiety and desire

This study develops and validates a model, based on personal cultural values theory and psychological research, in relation to technology adoption. The model focuses specifically on the future use of on-demand air mobility (ODAM), which is expected to have significant implications for city commuting and personal well-being in the years ahead. We use a path modelling approach, in addition to recently advanced analytical methods such as the finite mixture partial least squares (FIMIX-PLS), measurement invariance of composite models (MICOM) and multi-group analysis, to validate the model using a data set of 627 young consumers from the Czech Republic. The research model explains 45.2% variation in the future use of ODAM using our global model. This variance explained in the future use of ODAM increases to 62.3% and 64.5% respectively, when we segment our data set into two groups. The results also show that tradition has significant influence on technology anxiety, personal innovativeness and desire to use ODAM. Independence positively affects personal innovativeness but not the desire to use ODAM. We also find that technology anxiety influences the desire to use, which in turn influences the future use of ODAM. However, we find group differences in the influence of ambiguity intolerance on technology anxiety, desire and personal innovativeness. Thus, the study also evaluates the existence of significant differences between two groups in our data set. Overall, the study suggests that individual cultural values play a particularly important role in influencing the future use of ODAM through psychological characteristics. The research implications of the study are discussed in the article.     

The role of social networks in the adoption of competing new technologies in Ghana

We use a detailed dataset to examine the impact of social networks, conditional on contextual and individual confounders, on farmers' adoption of competing improved soybean varieties in Ghana. Based on the contagion conceptual framework, we employ a spatial autoregressive multinomial probit model to examine how neighbours' varietal and cross-varietal adoption of improved varieties affect a farmer's adoption decision in the social network. Our results show that adoption decisions in a network tend to converge on one variety, such that beyond a threshold of adopting neighbours of that improved variety, the cross-varietal effects tend to lose significance in the network. If the shares of adopting neighbours of the improved varieties are equal, we find evidence that farmers are not more likely to adopt either improved variety compared to farmers with no neighbours who have adopted the improved varieties. The findings demonstrate the significance of neighbourhood effects in the adoption of competing technologies.     

Utilising farm-level panel data to estimate climate change impacts and adaptation potentials

We combine farm accounting data with high-resolution meteorological data, and climate scenarios to estimate climate change impacts and adaptation potentials at the farm level. To do so, we adapt the seminal model of Moore and Lobell (2014) who applied panel data econometrics to data aggregated from the farm to the regional (subnational) level. We discuss and empirically investigate the advantages and challenges of applying such models to farm-level data, including issues of endogeneity of explanatory variables, heterogeneity of farm responses to weather shocks, measurement errors in meteorological variables, and aggregation bias. Empirical investigations into these issues reveal that endogeneity due to measurement errors in temperature and precipitation variables, as well as heterogeneous responses of farms toward climate change may be problematic. Moreover, depending on how data are aggregated, results differ substantially compared to farm-level analysis. Based on data from Austria and two climate scenarios (Effective Measures and High Emission) for 2040, we estimate that the profits of farms will decline, on average, by 4.4% (Effective Measures) and 10% (High Emission). Adaptation options help to considerably ameliorate the adverse situation under both scenarios. Our results reinforce the need for mitigation and adaptation to climate change.     

Ramsey rule with forward/backward utility for long-term yield curves modeling

Decisions in Economics and Finance - This paper draws a parallel between the economic and financial points of view in the modeling of long-term yield curves and provides new results on asymptotic...     

ON URBAN RE-ARRANGEMENTS: A Suite in Five Movements

This movement introduces the ethos of the collective project: its conceptual and practical preoccupations. It focuses on our concern with urban processes on the cusp of change, in the midst of being re-arranged, and thus homes in on the various polyrhythms of intersections, how things come together and diverge, how possibilities open and close in urban contexts of continuously shifting horizons.