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In this study, the authors provide a systematic literature review of articles in the emerging areas of green finance and discuss the status and challenges in sustainability disclosure, which is crucial for the efficiency of green financial instruments. The authors then review the literature on the economic implications of green finance and outline future research directions.

The authors use the analytical framework – Search, Appraisal, Synthesis, and Analysis (SALSA) to conduct the systematic review of the literature.

Increasing public attention to the environment motivates the use of green finance to fund environmentally sustainable projects, and the rise of green finance intensifies the demand for environmental disclosure. Literature has documented tremendous growth in sustainability reporting over time and around the globe, as well as raised concerns about how such reporting lack consistency, comparability, and assurance. Despite these challenges, the authors find that in general, the literature agrees that a firm’s green practice is positively associated with its financial performance and negatively related to a firm’s cost of capital. Green finance is also found to bring about enhanced risk management and economic development.

The authors provide one of the first reviews of green finance, sustainability disclosure and the impact of green finance on financial performance, capital market and economic development.

This paper aims to analyze how decision support systems manage Big data to obtain value.

A systematic literature review was performed with screening and analysis of 72 articles published between 2012 and 2019.

The findings reveal that techniques of big data analytics, machine learning algorithms and technologies predominantly related to computer science and cloud computing are used on decision support systems. Another finding was that the main areas that these techniques and technologies are been applied are logistic, traffic, health, business and market. This article also allows authors to understand the relationship in which descriptive, predictive and prescriptive analyses are used according to an inverse relationship of complexity in data analysis and the need for human decision-making.

As it is an emerging theme, this study seeks to present an overview of the techniques and technologies that are being discussed in the literature to solve problems in their respective areas, as a form of theoretical contribution. The authors also understand that there is a practical contribution to the maturity of the discussion and with reflections even presented as suggestions for future research, such as the ethical discussion. This study’s descriptive classification can also serve as a guide for new researchers who seek to understand the research involving decision support systems and big data to gain value in our society.

There is a pressing need to increase investments in sustainable infrastructure to promote low carbon economic growth and ensure environmental sustainability. Consequently, this study examines the socio-political factors underlying the adoption of green bond financing of infrastructure projects.

Primary data was gathered from experts with advanced experience in, or knowledge of green bonds in the Kumasi Metropolis. To identify respondents with pertinent knowledge that is relevant to the study, purposive and snowball sampling techniques were used. One-sample t-test and relative importance index were used in this study's statistical analysis.

‘Training and experience with sustainable finance’ was seen as the most important social factor underlying the adoption of green bond financing of infrastructure projects by the respondents and ‘Governmental tax-based incentives’ was rated as the leading political factor.

This pioneering research attempts to ascertain the socio-political factors affecting the adoption of green bond financing of infrastructure projects. Emergent results of analysis and concomitant discussions add knowledge to fill a void in literature on the social and political factors affecting the adoption of green bond financing of infrastructure projects in developing countries.

The present paper aims to evaluate the structural impact of exogenously induced fiscal shocks on the Moroccan economy. This entails an analysis of the effect on the GDP of COVID-19-induced fiscal shocks manifesting in terms of budgetary revenues and expenditures. A key aspect of this analysis addresses the size of the tax and fiscal multipliers.

The study examines the structural relationship between five variables during the period between Q1 2009 and Q2 2020 using an SVAR approach that allows for a dynamic interaction between ordinary expenditures and revenues on a quarterly basis.

Positive structural shocks on public spending are likely to negatively impact economic growth. Negative economic growth, in turn, will damage price levels and interest rates, mainly over the long term. However, public-revenue-multiplier-associated shocks exceed these price- and interest-rate multiplier-associated shocks. Indeed, a structural shock to ordinary revenues can have a positive but insignificant impact on the GDP stemming from the ensuing decrease in the government budget deficit that proceeds from the increase in government revenues.

This is one of the first studies in the Moroccan context to assess the impact of the current worldwide pandemic on public finances. In addition, this study highlights the importance of boosting economic recovery through public spending.