This study focuses on the impact of trade in environmental goods (green trade) on the environment. We found that green trade can decrease pollution levels by exploiting a panel of 277 Chinese cities from 2004 to 2013 and using the instrumental variable (IV) strategy. However, total trade openness is far less favorable to the environment. We also found that both green imports and exports are conducive to the Chinese environment, while ordinary green trade performs better than green processing trade. Nevertheless, the effects of green trade are restricted by a city's purchasing power and absorptive capacity, as well as the classifications of environmental goods. Furthermore, green trade mainly promotes local green technological progress to benefit the environment.

Green credit policy (GCP) relies on financial means to promote environmental governance. Whether it can achieve the goals of economic development and environmental protection, especially in the context of different institutional supplies, remains to be scientifically tested. Based on the implementation of China’s Green Credit Guidelines in 2012, this study uses panel data of Chinese companies from 2009 to 2019 to explore the influence of GCP on green technology innovation and the role of institutional supply in it. The results show that GCP is instrumental in promoting green innovation in heavily polluting enterprises, and the promotion effect is heterogeneous based on green patent types, firms’ ownership, and regional financial development levels. Further analysis finds that the supply of environmental protection systems by local governments can strengthen the green innovation effect of GCP. However, the institutional supply of innovation has not yet released a promotional effect. This paper finds that green credit can be used as an environmental governance tool and provides inspiration for local governments to issue environmental protection policies scientifically.

The high price of energy due to green energy policy will cause adjustments across the US economy predicted in the present general equilibrium model that includes energy Btu input with capital and labor to produce manufactures and services. This same model in trade theory examines the effects of a tariff on an imported factor of production such as a natural resource or capital.  Corresponding error correction estimates of the reduced form equations of the model in annual 1970-2018 data prove robust and suggest model modifications. A parametric approach to noncompetitive pricing based on unit cost diminishing with output brings the model much closer to the estimates. Manufactures are revealed to have a higher degree of noncompetitive pricing than services. Assuming constant elasticity of substitution production, a weak degree is revealed to provide the best fit to the error correction estimates. The high price of green energy will cause an inelastic decrease in energy input resulting in increased energy revenue. Outputs of both sectors fall, more in manufactures due to its energy intensity. The capital return and wage also fall given the weak substitution of capital and labor relative to the price of energy. The wage rises slightly in the model but falls considerably in the error correction estimate. The only clear winner is energy input with increased revenue. The government owns a large share of hydrocarbon reserves and will benefit from a higher price of energy.

Green innovation, driven by China's new development concept, plays a crucial role in high-quality economic development. In line with the green development trend, businesses increasingly prioritize whether their corporate environmental responsibilities (CER) can effectively enhance corporate green innovation (CGI) levels. This paper examines the influence and mechanism of CER on CGI using a dynamic perspective, drawing from 1,640 manually-collected panel data of Shanghai and Shenzhen A-share listed companies between 2010 and 2017. The primary findings indicate that the impact of CER on CGI possesses phase-specific characteristics and a dual effect of "crowding in" and "crowding out." The current phase of CER negatively affects green innovation, while the lag phase has a positive effect. CER's impact on various CGI types is heterogeneous: specifically, it follows an "inverted-N" trajectory (inhibition-promotion-inhibition) for "strategic green innovation" and has a promotional effect on "substantive green innovation," which is stronger and has a longer time lag. The mechanism analysis reveals that financing constraints play a critical mediating role. A heterogeneity analysis based on multiple dimensions (ownership, industry, and location) suggests that CER has a more significant driving force for CGI among state-owned firms, high-polluting industries, and enterprises in inland areas. Finally, the paper presents corresponding suggestions for government and corporate entities.

Green-biased technological progress takes into account the influence of energy input and pollution emissions, which is of great significance to China's green development. This paper decomposes technological progress into two categories: green input-biased technological progress (IBTC) and green output-biased technological progress (OBTC), using the Slacks-based measure integrating (SBM) model. The factor bias in technological progress is determined based on data from 34 industries in China from 2000 to 2015. The results show that green-biased technological progress exists significantly in the industry, and most of it promotes the growth of green total factor productivity. IBTC first tends to consume energy to pursue capital between capital input and energy input, while it tends to save energy after the Eleventh Five-Year Plan. Between labor input and energy input, it is biased towards saving labor and consuming resources. OBTC is biased towards promoting industrial growth and curbing pollution emissions. Medium and light-polluting industries are biased toward promoting industrial growth and curbing pollution emissions, while heavy-polluting industries are biased towards emitting more pollution.

The diplomatic relations between Azerbaijan and Pakistan have undergone significant development since the late 1990s. Despite some studies focusing on diplomatic and political relations, the economic dimension of this partnership has not been systematically covered. In this paper, a robust least squares (RLS) approach is used to model bilateral trade between the two countries and identify statistically significant determinants, including economic, institutional, and cultural factors, after analyzing the main economic and subsectoral trade dynamics of both countries. The results reveal that Azerbaijan's imports from Pakistan are significantly associated with the country's higher income and market size, while Pakistan's elevated exchange rate and trade costs hinder bilateral trade. In addition, Pakistan's rising manufacturing output increases exports to Azerbaijan but decreases imports from Azerbaijan. Among institutional determinants, government effectiveness, the trade freedom index (TFI), and political stability play significant roles in bilateral trade. Lastly, it was found that cultural proximity increases Pakistani exports to Azerbaijan but not imports from Azerbaijan. The analysis also indicates that domestic savings stimulate Pakistani exports. These findings underscore the complex nature of bilateral trade between Azerbaijan and Pakistan and highlight the need for institutional arrangements to harmonize trade flows and boost welfare gains from trade. The results may be of great importance to intergovernmental working groups, domestic policymakers, and businesses.

This paper examines whether sell-side analyst-affiliated investors trade stocks before their analysts release material information. Using class action lawsuits, I explore the pre-lawsuit periods and investigate how potentially informed analysts trade their covered firms. The event study finds that analyst-affiliated investors reduce their stockholdings of firms prior to their own analyst downgrades. The findings are more pronounced among investors employed by investment banks. The post-trading performance of analyst-affiliated investors suggests that they have superior information and front-run to maximize benefits.

This study investigates the impact of financial inclusion on environmental sustainability in 178 emerging and developing countries from 1996 to 2022. Employing a composite index derived through Principal Component Analysis (PCA) as a measure of financial inclusion and covering four aspects (access, depth, efficiency, stability), our analysis reveals negative outcomes. The findings indicate that enhancing financial inclusion is associated with a notable increase in CO2 per capita emissions as well as in Total Greenhouse Gas emissions. We demonstrate that controlling corruption improves environmental quality, yet this measure alone is insufficient to fully mitigate the impact of financial inclusion, as indicated by our moderation analysis. The same analysis, however, shows that fostering globalization through trade openness is an efficient tool to alleviate the positive effect of financial inclusion on the quality of the environment. The study employs various policies targeting the control of development levels, energy consumption, natural resource utilization, industry, and urban population dynamics to contextualize the influence of financial inclusion on environmental sustainability. Through econometric methods and a comprehensive examination of the specified time frame, our results provide insights into the complex interplay between financial inclusion and environmental outcomes in diverse socio-economic contexts. The research contributes to the discourse on sustainable development by highlighting the potential of certain factors as a catalyst for environmental improvement. Understanding these dynamics is crucial for policymakers, as it underlines the trade-off between integrating inclusive financial strategies and achieving environmentally sustainable development trajectories in emerging and developing nations. Moreover, shedding light on the underlying mechanisms, such as trade-offs, fills a significant gap in the literature.

This paper presents an operational framework for assessing the trajectories of production, energy, emissions, and capital accumulation to ensure the implementation of Nationally Determined Contributions (NDCs). The framework combines widely used methodologies (STIRPAT, system dynamics, and optimization) to simulate the pathways of variables until a target year. The CO-STIRPAT dynamic system allows us to identify the spillover pathways from carbon policy to economic growth based on output optimization principles; to conduct a more systematic analysis of the interconnections between the main drivers that determine carbon emissions; to develop a cost-effective climate policy mix that is a backbone for the right combination of carbon pricing, energy efficiency, and carbon intensity; and to assess NDC targets with respect to ambition gaps, implementation gaps, and feasibility.

In reality firms most often face negatively sloped demand curves. Then, for a given level of consumers’ surplus, levies on prices yield higher fiscal revenues than specific duties. Therefore, according to the prevailing view, the switch from unit to ad valorem taxation is supposed to generate more welfare; some even speak of an associated Pareto-improvement. However, this is not true because taxing prices merely transfers profits to the Treasury, while total rent remains unaffected. Since excise duties diminish the welfare gain in comparison with untaxed trade, an appropriately designed income tax allows all parties to benefit. Sales should be taxed only exceptionally.

Objective: The paper, specifically examines whether the recently formed Regional Comprehensive Economic Partnership (RCEP) can potentially facilitate minimising the constraints to renewable energy goods exports at the regional level in Asia. Data, Methodology and Findings: Using the panel data from 11 RCEP members from 2006 to 2014, this study has applied the ‘meta frontier stochastic gravity frontier’ methodology and confirms that the establishment of RCEP has the potential to improve trade in renewable energy commodities within the RCEP region. Policy Implications: The policy implication is that when countries work together, it will lead to enormous benefits for national, regional, and worldwide prospects of a more sustainable energy future. Practical Policy Implications: In terms of practical policy implications, the developed RCEP member countries should actively engage in promoting R&D activities and protecting intellectual property rights concerning renewable energy production, which are essential for countries to integrate with the world market and to lift the export frontiers of both the developed and developing RCEP member countries to reach the unrestricted export of renewable energy technology.

Credit scoring remains one of the most important subjects in financial risk management. Although the methods in this field have grown in sophistication, further improvements are necessary. These advances could translate in major gains for financial institutions and other companies that extend credit by diminishing the potential for losses in this process. This research seeks to compare statistical and artificial intelligence (AI) predictors in a credit risk analysis setting, namely the discriminant analysis, the logistic regression (LR), the artificial neural networks (ANNs), and the random forests. In order to perform this comparison, these methods are used to predict the default risk for a sample of companies that engage in trade credit. Pre-processing procedures are established, namely in the form of a proper sampling technique to assure the balance of the sample. Additionally, multicollinearity in the dataset is assessed via an analysis of the variance inflation factors (VIFs), and the presence of multivariate outliers is investigated with an algorithm based on robust Mahalanobis distances (MDs). After seeking the most beneficial architectures and/or settings for each predictor category, the final models are then compared in terms of several relevant key performance indicators (KPIs). The benchmarking analysis revealed that the artificial intelligence methods outperformed the statistical approaches.

Stock indexes are used as a barometer of economic health, and fluctuations in these markets can have a substantial influence on the economy. For example, the Covid-19 pandemic caused severe economic disruption, as reflected in stock market indexes. Similarly, Russia's invasion of Ukraine has geopolitical implications that might undermine global trade and economic stability, impacting stock market indexes. Considering these developments, the purpose of this article is to examine the co-movements of the stock markets of Austria (ATX), Poland (WIG), the Czech Republic (PX Prague), Hungary (BUX), Croatia (CROBEX), Serbia (BELEX 15), Romania (BET), and Slovenia (SBI TOP) from February 16, 2018, to February 15, 2023. To achieve the research objectives, the aim is to answer the following research question: i) Have the events of 2020 and 2022 accentuated the co-movements between the stock markets in Central Europe? The results show the presence of 21 shocks between markets (out of a potential 56) during the Tranquil subperiod, with the WIG stock index having a greater predictive influence on the behavior of its peers (4 shocks out of 7 possible). During the Stress subperiod, 45 shocks were confirmed (out of 56 possible). The markets that triggered the most market shocks (7 out of 7 possibilities) were BET, BUX, CROBEX, and SBI TOP. The research question was validated based on the conclusion supplied, as all markets increased their movements, showing a considerable effect of the 2020 and 2022 events on these markets.

The purpose of this paper is to examine and elucidate the effect of the announcement of winning a US federal contract by pharmaceutical companies, traded on the NASDAQ stock exchange on the share prices. The analysis is based on a sample of 327 event days from 12 pharmaceutical companies listed on the NASDAQ market for a given period (2010 – 2021). The sample is analysed by using the event study methodology and Capital Asset Pricing Model (CAPM) in order to examine the abnormal returns and cumulative abnormal returns during an event window of 10 days before the event and 10 days after the event. While prior researchers have identified significant positive abnormal returns following different types of contract announcements, this study aims to contribute to the existing literature by exploring the specific impact of the public contract in the pharmaceutical market on market shares. By investigating this unique aspect, the research fills a gap in previous studies and enhances our understanding of the relationship between public contract announcements and market performance. The data collection utilises secondary data from the official open data source of US federal spending information and daily stock prices. The collected data is analysed quantitatively using event study methodology, allowing for statistical examination of abnormal returns, such as ANOVA analysis, t-statistic, beta index, time series analysis and linear regression. The research findings indicate a significant impact of public contract-winning announcements on cumulative abnormal returns, suggesting a positive reaction from the market. However, average abnormal returns within the event window were positive but not statistically significant. These results imply that winning a government contract has a limited impact on the stock market. The implications of this research highlight the importance of public contract announcements in the pharmaceutical industry and their influence on share prices. The findings provide valuable insights for investors and stakeholders, aiding decision-making processes related to investment opportunities and market performance.