Water investments play an increasingly important role in sustainable finance, yet their response to climate policy uncertainty (CPU) under different market conditions remains poorly understood. This study examines the regime-dependent influence of CPU on water equity performance using monthly data for the First Trust Water ETF (FIW) and the Invesco Global Water ETF (PIO) from 2007 to 2024. A Markov regime-switching VAR framework is employed to capture nonlinear dynamics that conventional linear models may overlook. The results reveal two distinct volatility regimes with contrasting CPU effects. In low-volatility periods, CPU is associated with higher returns, indicating that climate-policy developments can signal investment opportunities when markets are stable. During high-volatility periods, CPU exerts a negative influence, consistent with rising discount rates applied to long-term water-infrastructure cash flows. Regime persistence differs across ETFs: FIW exhibits frequent, short-lived transitions, whereas PIO displays more persistent states. A complementary DCC-GARCH analysis shows that crude oil provides a relatively cost-effective hedge for water portfolios, while technology ETFs offer substantially weaker hedging performance. Overall, the findings highlight the importance of regime-sensitive portfolio strategies for investors and emphasize that policymakers should consider prevailing market conditions when communicating climate initiatives. The study demonstrates that nonlinear models are essential for uncovering climate-finance linkages that linear approaches fail to detect. 

This paper reports on a field experiment investigating hiring discrimination in the Swedish labor market against job candidates signaling intentions to take part-time parental leave. Swedish law prohibits labor market discrimination related to parental leave. We conducted a correspondence test, sending job applications to 960 employers in one male-dominated and one female-dominated low-skilled occupation. Applicants who indicated plans for parental leave were significantly less likely to receive job interview invitations, with their chances cut by half compared to those who did not mention such plans. This bias emerged across genders and occupations, revealing a systemic issue in the labor market.

Readiness, riskiness, and renewables appear to form a "tripartite symbiosis" in the clean energy realm. Previous research underscores the significance of readiness as a prerequisite for a nation's advancement toward sustainable energy, urging careful navigation of uncertainties within this framework. Our research expands upon existing literature by delving into how country-level readiness influences a country's innovation in renewable energy in the face of uncertainty. Employing panel fixed effect threshold regression with four distinct models, we analyze this dynamic across 65 countries, representing both advanced and emerging economies. The results validate the presence of an uncertainty threshold effect across all model regressions, confirming a non-linear relationship among uncertainty, country-level readiness, and renewable energy innovation. Overall country-level readiness, along with its components-economic and social readiness-individually fosters renewable energy innovation under low uncertainty. However, this positive influence weakens as uncertainty exceeds the threshold. Conversely, governance readiness exerts a negative impact on renewable energy innovation under low uncertainty, with its detrimental effects becoming more significant at higher levels of uncertainty. The lagged uncertainty has a significant negative association with renewable energy innovation. Policymakers and investors should prioritize developing country level readiness to successfully manage the potential negative influence of uncertainties on renewable energy innovation.

The study presented in this paper examined intersectional discrimination against individuals with multiple minority identities in the housing market, specifically those identifying as Arab Muslims. By sending inquiries from three fictitious applicants - a Swedish Christian, an Arab Christian, and an Arab Muslim - to 1,200 landlords in Sweden, we analyzed differences in landlord responses. Results showed the Swedish Christian received the most positive replies, followed by the Arab Christian, with the Arab Muslim receiving the fewest. The study underscores the compounded discrimination faced by those with multiple minority identities and challenges the conflation of ethnic and religious identities in prior research.

While the process of carbon capture, utilization, and storage (CCUS) plays a pivotal role in mitigating climate change impacts, rising economic uncertainty, geopolitical conflict, and oil price volatility tend to retard CCUS deployment; which carbon emissions trading mechanisms can mitigate. The literature shows that such schemes are still immature in developing economies such as China, where carbon pricing seems to be a key strategy to lower CO2 power generation emissions. In this study, we thus investigate the Chinese carbon market’s volatility, concentrating on time-dependent jumps in emissions pricing. As jump-induced volatility represents an important risk, precise information thereon is important for increased carbon trading efficiency. The GARCH-jump process finds that such jumps do occur in the Chinese emissions market and that key uncertainty indicators including the aforementioned economic policy uncertainty, crude oil volatility index, and geopolitical risk can explain the resulting volatility, with important implications for policymakers and socially responsible investors.

While numerous empirical papers have investigated the volatility dynamics of Chinese clean energy equity markets, this is among the first studies to assess the impact of climate uncertainty on the risk levels of such assets. Given that China is extensively investing in green projects to achieve carbon neutrality, this strand of research offers important implications for investors and policymakers. Methodologically, we employ the GARCH-MIDAS model to examine the effect of the climate policy uncertainty (CPU) index on the volatility levels of the Chinese clean energy exchange-traded fund (ETF). We compare the effects of the CPU index with leading uncertainty indicators, including the crude oil volatility index, geopolitical risk, and technology sector volatility. The in-sample and out-of-sample analyses show that CPU has significant predictive contents for forecasting the volatility of renewable energy ETF and that the GARCH-MIDAS-CPU process outperforms other approaches. These results offer key implications for policymakers and socially responsible investors.

In ‘Discrimination as a Determinant of Economic Inequality’, Ali Ahmed, Mats Lundahl, and Eskil Wadensjö examine how economic theory can be used to unearth the mechanisms to produce discrimination and inequality, to identify the winners and losers from it, and to construct recipes for the eradication of discrimination. They stress the fact that economics is far from a unified social science and hence the need to apply different theories as different situations call for it. Two notorious cases are examined: the South African apartheid system and the American discrimination of blacks by whites. The chapter ends with an examination of a number of contemporary cases of ethnic discrimination of minorities.

China is promoting carbon neutrality to cope with environmental degradation and economic loss, issuing more green bonds than any other country to finance its green transformation. Uncertainty affects green bonds more than traditional bonds, including policy uncertainty, natural disasters, and energy crises. This chapter advocates green bond underlying volatility swaps, a derivative that allows investors to trade green bond price volatility, which market participants can use for hedging. We propose a framework for forecasting realized volatility by demonstrating that Chinese green bonds are highly homogeneous, making them useful in such forecasting and thus guiding trading in such swaps. We also examine the forecasting performance of a novel model, RVNET-GARCH, which synthesizes multiple green bonds’ historical realized volatility into a network factor. Testing for robustness with three Monte Carlo simulations and six rolling horizons shows that the proposed methodology can provide reliable results.

We analyse time-varying risk spillover and dependence to assess the systemic risk benefits of ethical, sustainable, and green investments. Our data comprise sustainable investments from ethical, environmental, social and governance (ESG), and green bonds. We investigate the link to major asset classes, including equity, commodity, and currency markets. We find evidence of close connection between the major asset classes and sustainable assets, except green bonds. We also explore the improvement in hedging efficiency from combining ethical and ESG investments with commodities and currencies over investment horizons. Our analysis based on systemic risk measures indicates that there is evidence of lower time-scale systemic risk connectedness in the case of commodities and currencies combined with ethical and ESG assets. These findings have significant implications for portfolio managers, policymakers, and market participants.