Search Results (188)

Along with the new stage of prevention and control of the COVID-19 pandemic and the vision and goals of combatting climate change, the challenges of the transition to a green economy have become more severe. The need for green recovery of the economy, stability and security of energy production and consumption, and the coordination of low-carbon transformation and socio-economic development has become increasingly urgent. This paper proposes a new theoretical framework to study the effect of carbon emission reduction on the mutual application of the carbon market, fiscal policy and monetary policy under the non-homothetic preference of energy product consumption. By constructing an environmental dynamic stochastic general equilibrium (E-DSGE) model with residents’ non-homothetic preferences, this paper finds that coordinating the carbon market and macroeconomic policies can achieve economic and environmental goals. However, the transmission paths for each are different. The carbon market influences producers’ abatement efforts and costs through carbon prices. Monetary policy controls carbon emissions by adjusting interest rates, while fiscal policy controls carbon emissions by adjusting total social demand. Improving non-homothetic preferences will amplify business cycle fluctuations caused by exogenous shocks, thus assuming the role of a “financial accelerator”. Further research shows that non-homothetic preferences influence the heterogeneity of different policy mixes. Finally, this paper discovers that the welfare effects, the relative size and difference of long-term and short-term effects resulting from the different policy mixes, also depend on the level of non-homothetic preferences. The intertemporal substitution mechanism due to the improvement of non-homothetic preferences endows low-carbon production with “option” characteristics. Our study reveals the role of non-homothetic preferences on the effectiveness of policy implementation. It highlights the importance of matching monetary and fiscal policies with the carbon market based on the consumption and production side. It provides ideas for policy practice to achieve the goal of “dual carbon” and promoting coordinated socio-economic development. Full article

This paper analyses Keynes’s 1930 prediction that technical advances would cut people’s working week to 15 h by 2030 and investigates why actual working hours are significantly higher in the United States. Elaborating on Keynes’s forecast to provide a general productivity formula while keeping its simplicity, we ran tests on macro-data from 1929 to 2019 and on estimates for 2030, demonstrating that productivity is surprisingly still insufficient to allow for a reduction in working hours across the US economy. This finding represents a substantial contribution to the literature, which has mostly explained long working hours by means of new consumer needs. Even by using microdata, we show that consumption does not explain the stickiness of working hours to the bottom. Hence, this paper combines a macroeconomic, logical-analytical approach based on historical time series with rigorously constructed time series at the microeconomic level. Finally, we also provide policies to narrow the productivity differential to Keynes’s prediction for 2030 while fostering work-life balance and sustainable growth. To understand long working hours in the US despite technical advances—this being one of our main findings—productivity remains crucial. Full article

Global efforts to build sustainable e-commerce ecosystems through various prediction tools have suffered due to uncertainty in politics, the economy, and the environment. This paper proposes a new integrative prediction model to track the sustainable development of e-commerce. Using US e-commerce data, this study explores the prediction accuracy of the mixed data sampling (MIDAS) model in combination with the attention mechanism (AM) approach, analyzing the performance differences between the model’s before and after improvements. More so, the paper evaluates the performance of the new prediction approach against other competing models using the prediction accuracy metric, the probability interval test, and the Diebold and Mariann (DM) test methods. The results indicate that the introduction of the MIDAS and the AM approaches allows the prediction model to fully utilize the effective information of the mixed-frequency data while simultaneously capturing the differences in the importance of the variables in terms of their time series and the non-linear relationship of the learning variables, thereby positively influencing the economic prediction of the e-commerce industry. Second, the proposed prediction model combines the ability of long-term and short-term high-precision prediction and performs multi-step probability prediction on the development of the e-commerce industry. It can better track abnormal changes in macroeconomic indicators and fit their fluctuation trends. Third, based on the results of the three evaluation indicators, the MIDAS–AM–Deep autoregressive recurrent neural network (DeepAR) model achieves optimal prediction accuracy, allowing it to provide more timely, accurate, and comprehensive predictions for e-commerce management decisions when macroeconomic conditions are undergoing significant transformations. Full article

Amidst the tremendous evolution of the digital economy and the expedited establishment of a new development paradigm, the use of artificial intelligence (AI) technologies holds significant importance in achieving superior economic development. While much of the previous research focused on the macroeconomic impact of AI, this study examined how AI technology affects food processing firm performance, productivity, and labor skill structure at the food processing firm level. This study utilized panel data from listed food processing enterprises in Shanghai and Shenzhen spanning from 2010 to 2021, performing textual analysis on the annual reports of listed companies and then creating enterprise-level AI indicators to empirically examine the influence of AI applications on enterprise performance and its underlying mechanisms. The findings indicate a substantial improvement in business performance due to the application of artificial intelligence, which is a conclusion corroborated through a series of stability tests. Exploring channels and mechanisms, the analysis revealed that AI-driven advancements in production technologies stimulated the requirement for highly skilled labor, thereby inducing shifts in the labor force’s structure. Further investigation demonstrated that artificial intelligence contributed to enhancing the total factor productivity, consequently bolstering the overall enterprise performance. A heterogeneity analysis showed that firm-level factors, such as the nature of property rights and factor intensity, had an impact on the influence of AI on firm performance. In addition, the geographic location and time of year of a company also had impacts on the productivity benefits of artificial intelligence. This research deepened the cognition and understanding of the role played by AI in the production process at the micro-enterprise level and provided suggestions for promoting the development of artificial intelligence technologies at the micro-enterprise level, which will facilitate the transformation of the labor structure to further augment enterprise efficiency. Full article

According to the IMF, the current public debt makes up nearly 40 percent of the global debt, marking the highest share since the mid-1960s. Despite the vast research on alarming levels of sovereign default, the literature remains inconclusive. This paper investigates macroeconomic, financial, and political determinants of IMF credit use in the post-2010 era. The main contribution of our study lies in its temporal analysis as we investigate how the robustness of different factors has evolved. By utilizing an extensive dataset on 216 countries over the period of 2010–2021 and employing a variant of the Extreme Bounds Analysis (EBA) method, our study reveals that fluctuations in the IMF credit to external debt ratio can be attributed to changes in the total reserves to external debt ratio, where this relationship is statistically significant and reliable. However, high political risks seem to no longer affect the IMF’s decision, post 2010. Furthermore, our findings demonstrate that excluding countries with low debt arrears strengthens the results’ robustness. These findings contribute to a better understanding of the complexities surrounding IMF credit use in the contemporary global economic scene and offer new standpoints on the Fund’s lending choices. Full article

A new approach based on input–output (IO) analysis has emerged to estimate the carbon footprints of companies and their products from cradle to gate. While the approach relies on the same principles as the GHG Protocol, it uses a distributed iterative framework to improve the footprint estimations and reduce their uncertainty. While optimal estimations would result if all the world’s companies would enter such a system, this paper shows how such a distributed system could apply to the real world where many enterprises would stay out of the system. We show how the quality of the estimations with respect to the GHG Protocol would be increased by integrating scope 1 and scope 2 data from the value chains in the footprint estimations and progressively reducing the part of the remaining scope 3 data. To help with analyzing uncertainty, we show how to use the scope 1/2/3 decomposition to estimate the biases and the standard deviations of the computed production carbon intensities. We illustrate the model on macroeconomic data for 44 sectors and two regions (Europe and Rest of World), using the Inter-Country Input–Output database from the OECD. Such a system would necessarily rely on Information and Communication Technology, since the companies would be permanently interconnected in a large-scale meshed network, using an application protocol for data exchange. Full article

The sustainability-linked discussion has gained international importance, with the banking sector being an essential pillar of the new economy, particularly through channeling financial resources to environmentally friendly economic activities. It is, however, still unclear if ESG is profitable, both for the economy and banks. This paper aims at filling this gap by presenting, from a macroeconomic perspective, the impact of ESG efforts and the banking sector’s contribution to a sustainable economy. Using panel regression models with fixed effects, the study investigates the impact of ESG factors and banking activity on economic growth. The results show a negative relationship between country-level ESG scores and economic growth, both in the short and long run, while increased financial intermediation by the banking sector, used as a proxy of potential green lending activity, does not necessarily enhance economic growth. Through delving into the interplay between the ESG score, economic development, and banking activity, this research could serve as a discussion point for economists, bankers, and policymakers when designing the economic and financial strategies for transitioning to a green economy. Full article

Smart buildings play a key role in the complex ecosystem of cities and are often subject to barriers that limit their development. Although identifying these barriers is fundamental to creating an enabling environment for this segment’s expansion, few works aim to identify these challenges. This work has two main objectives: (1) to research the main barriers limiting the development of new smart building projects and (2) to prioritize these barriers from the perspective of professionals with experience in the field. We adopted an exploratory approach common in research that focuses on identifying and prioritizing variables related to a phenomenon, which is based on two main actions: obtaining information through a careful literature review and consulting professionals who work in the concerned field. The results showed that professionals assessed the 23 barriers identified through bibliographic research as important, with the most important being related to lack of qualified professionals, shortage of government policies, higher initial and construction costs, macroeconomic barriers and access to financing, high cost of intelligent systems and technologies, regulatory barriers, lack of knowledge about the current and potential benefits of smart buildings, and more complex design and construction. Full article

This article analyzes the phenomenon of deconsumption, which is relatively new and insufficiently researched or defined. Based on a review of the literature on the subject, it was found that there was little interest in deconsumption compared with sustainable consumption. Moreover, the number of scientific publications was negligible as the concept of deconsumption was rarely studied as a phenomenon. In addition, it should be noted that deconsumption can play a role in sustainable development and care for the environment and natural resources. Our study on this phenomenon sought to determine whether the phenomenon has spread to a noticeable degree, despite the deconsumption trend, which is important for its effective popularization in societies. Therefore, the aim of this study was to analyze whether the deconsumption phenomenon was reflected in macroeconomic data on consumption in selected European Union countries prior to the COVID-19 pandemic. The analysis of macroeconomic data on per capita consumption in the years 2000–2019 did not reveal a clear phenomenon of deconsumption; however, changes in the consumption structure were identified. In some countries, consumption fluctuated or decreased in certain sectors, suggesting the possible emergence of deconsumption. The computation method used in this study was fuzzy c-means clustering (FCM). Full article

The development of predictive approaches to estimate supplier delivery risks has become vital for companies that rely heavily on outsourcing practices and lean management strategies in the era of the shortage economy. However, the literature that presents studies proposing the development of such approaches is still in its infancy, and several gaps have been found. In particular, most of the current studies present approaches that can only estimate whether suppliers will be late or not. Moreover, even if autocorrelation in data has been widely considered in demand forecasting, it has been neglected in supplier delivery risk predictions. Finally, current approaches struggle to consider macroeconomic data as input and rely mostly on machine learning models, while deep learning ones have rarely been investigated. The main contribution of this study is thus to propose a new approach that for the first time simultaneously adopts a deep learning model able to capture autocorrelation in data and integrates several macroeconomic indicators as input. Furthermore, as a second contribution, the performance of the proposed approach has been investigated in a real automotive case study and compared with those studies resulting from approaches that adopt traditional statistical models and models that do not consider macroeconomic indicators as additional inputs. The results highlight the capabilities of the proposed approach to provide good forecasts and outperform benchmarks for most of the considered predictions. Furthermore, the results provide evidence of the importance of considering macroeconomic indicators as additional input. Full article

We revisit, and document new facts regarding, the financialization of U.S. energy markets in 2000–2010. We show that, after controlling for macroeconomic factors and physical energy market fundamentals, the strength of energy markets’ co-movements with the U.S. stock market is positively related to the energy paper market activity of hedge funds that trade both asset classes. This relation weakens when credit risk is elevated. We find, in contrast, no link with the aggregate positions of commodity index traders in energy futures markets. Our findings have implications for the ongoing debate regarding the financialization of commodities. Full article

This study investigates the determinants of trading activity in the U.S. corporate bond market, focusing on the effects of Seasonal Affective Disorder (SAD) and macroeconomic announcements. Employing the General-to-Specific (Gets) Autometrics methodology, we identify distinct behavioral responses between retail and institutional investors to SAD, noting a significant impact on retail trading volumes but not on institutional trading or bond returns. This discovery extends the understanding of behavioral finance within the context of bond markets, diverging from established findings in equity and Treasury markets. Additionally, our analysis delineates the influence of macroeconomic announcements on trading activities, offering new insights into the market’s reaction to economic news. This study’s findings contribute to the broader literature on market microstructure and behavioral finance, providing empirical evidence on the interplay between psychological factors and macroeconomic information flow within corporate bond markets. By addressing these specific aspects with rigorous econometric techniques, our research enhances the comprehension of trading dynamics in less transparent markets, offering valuable perspectives for academics, investors, risk managers, and policymakers. Full article

Appropriately designed and implemented climate mitigation actions have multiple co-benefits (yet some trade-offs cannot be excluded) that result in substantial social and economic value beyond their direct impact on reducing energy consumption and GHG emissions. Despite their wider acknowledgement by the research community, decision makers and the public have incomplete information on these multiple effects. This paper has a twofold objective: First, through analytical bottom-up approaches, it assesses, in quantitative terms, the macroeconomic effects and the public health benefits attributed to a variety of mitigation actions under consideration in the context of the Greek Energy and Climate Plan. Second, it investigates, through a social survey, how citizens perceive climate change and value these multiple impacts of mitigation actions, and to what extent they are willing to pay for them and support the adoption of policy measures aiming at the green transition of the Greek economy. We show that mitigation actions bring about significant health benefits, particularly in cities, and generate significant positive macroeconomic effects, particularly if mitigation actions focus on the decarbonization of the building sector and on the exploitation of local renewable sources. We also argue that most people do not realize that climate mitigation actions can have wider benefits for society, such as tackling energy poverty, improving public health, and creating new jobs. Unwillingness to pay tends to be the prominent attitude. People who are more reluctant to cover a part of the cost of environmental protection are less likely to perceive that climate change is one of the main challenges at global and national level and support the adoption of climate mitigation policies. In this context, the national strategy for climate change should focus on effectively informing and engaging the public in climate mitigation strategies, strengthening the public trust in government institutions, promoting mutually acceptable solutions with the local communities, and providing incentives for changing citizens’ behavior towards climate-related actions. Full article

Price markups and firms’ Tobin’s Q ratios are widely believed to have been increasing in the past several decades. Various models for the calculation of price markups have been developed, each relying on the historically held definition of the ratio of price to marginal cost; however, all of these have methodological drawbacks, and some of the results they have produced have been poorly reflective of the near past wider macroeconomic experience. This paper defines a new approach for the definition and measurement of markup pricing, and it also avoids some of the issues surrounding the marginal cost approaches by using the measure of economic rent and the capital asset pricing model. The results show limited markup pricing for the UK’s FTSE 100 companies (2018–2023), but that certain real estate, technology/media and financial services/equity investment firms have enjoyed higher price markup levels. An analysis of the business models of these firms is used to qualitatively propose explanations for such markups. This work offers formal proof that that the expected price markup is equal to Tobin’s Q and finds that the empiric market level of markup is near equivalent to the market Tobin’s Q; the differences between the markup and Tobin’s Q at the level of the firm are equally assessed. This work challenges the general consensus that price markups are above one and have been increasing; it may also aid policy makers with respect to taxation policy and regulatory measures, as well as the financial management of firms in decisions concerning capital deployment and portfolio management. The method merits expansion to wider data sets, as well as to those from outside of the UK. Full article

In this study, we explored the impact of bank leverage and financial frictions on the transmission of real and financial shocks. Two new Keynesian dynamic stochastic general equilibrium (DSGE) models, with and without financial frictions, were employed in the context of the Tunisian economy. In the analysis, we considered three types of shocks—productivity, monetary, and adverse bank capital shocks. The findings reveal that, in the model with financial frictions, the response of macroeconomic and financial variables to demand and supply shocks was more pronounced than in the baseline model, where frictions primarily exist at the borrower level. In this study, we underscored the significance of financial shocks, particularly negative bank capital shocks, in triggering substantial macroeconomic and financial fluctuations, especially when banks operate with higher leverage ratios. Additionally, the inclusion of financial frictions in the DSGE model enhanced its ability to capture the empirical features of real and financial shocks, providing valuable insights for effective monetary policymaking. The results provide foundational insights for Tunisian policymakers to assess the impact of financial frictions in the context of the Tunisian economy. This is significant for the Central Bank of Tunisia, which has not yet adopted a specific DSGE model. Therefore, through our analysis, we determined the amplificatory role of financial frictions in the dynamics of macroeconomic and financial variables in Tunisia and examined the main transmission channels of shock propagation. Full article