Carbon risk has generated significant adverse impacts on firms, investors and other stakeholders. Carbon disclosure may provide market participants with information to effectively manage risks and explore opportunities. We conduct a critical review of the growing literature in these fields and seek to examine the financial effects of carbon risk and carbon disclosure. A total of 78 papers, published in influential accounting, finance, business, economics and management journals between 2011 and 2021, are reviewed. We categorise the financial effects into four groups: financial performance, valuation relevance, cost of capital and risk profiles (measures). The proxies for carbon risk and carbon disclosure are summarised. This review demonstrates inconclusive relationships between carbon risk (carbon disclosure) and firms' financial measures. These inconclusive findings may result from different carbon risk (carbon disclosure) measures, financial performance measures, sample geographies, sizes and periods, and model specifications. This review further identifies and highlights future research opportunities in relevant areas and calls for more research work to understand the influence of climate change on firms' value and activities.

This paper aims to estimate the parameters of the time-fractional Black-Scholes (TFBS) partial differential equation with the Caputo fractional derivative by using the real option prices of the S &P 500 index options. First, the numerical solution is obtained by developing a high-order scheme with order ( $$3-\alpha $$3 - α ) for the time discretisation. Some theoretical analyses such as stability and convergence are presented in order to verify the efficiency and accuracy of the proposed scheme. Secondly, we employ a modified hybrid Nelder-Mead simplex search and particle swarm optimization (MH-NMSS-PSO) to identify the fractional order $$\alpha $$α and implied volatility $$\sigma $$σ of the TFBS equation, and explore the financial meanings of $$\alpha $$α under extreme stock market conditions such as the Covid-19 and the 2008 global financial crisis. We analyse the values of $$\alpha $$α and compare the mean squared errors of both the TFBS model and the BS model. Our empirical results show that $$\alpha $$α may be regarded as a market fluctuation indicator for classifying financial environments, and the TFBS model is more capable of fitting real option data compared with the BS model, especially for put options during the economic downturn. In addition, we find and discuss an interesting relation between $$\alpha $$α and $$\sigma $$σ from both the TFBS model and the BS model in three expressions, which could be an open problem for further research.

Precise navigation in agricultural applications necessitates accurate guidance from the seedling belt, which the Global Positioning System (GPS) alone cannot provide. The overlapping leaves of Chinese cabbage (Brassica pekinensis Rupr.) present significant challenges for seedling belt fitting due to difficulties in plant identification. This study aims to address these challenges by improving the You Only Look Once (YOLO) v7 model with a novel approach that decouples its network head deriving from the Faster-Regions with Convolutional Neural Network (Faster R-CNN) architecture. Additionally, this study introduced a BiFormer attention mechanism to accurately identify the centers of overlapping Chinese cabbages. Using these identified centers and pixel distance verification, this study achieved precise fitting of the Chinese cabbage seedling belt (CCSB). Our experimental results demonstrated a significant improvement in performance metrics, with our improved model achieving a 2.5% increase in mean average precision compared to the original YOLO v7. Furthermore, our approach attained a 94.2% accuracy in CCSB fitting and a 91.3% Chinese cabbage identification rate. Compared to traditional methods such as the Hough transform and linear regression, our method showed an 18.6% increase in the CCSB identification rate and a 17.6% improvement in angle accuracy. The novelty of this study lies in the innovative combination of the YOLO v7 model with a decoupled head and the BiFormer attention mechanism, which together advance the identification and fitting of overlapping leafy vegetables. This advancement supports intelligent weeding, reduces the reliance on chemical herbicides, and promotes safer, more sustainable agricultural practices. Our research not only improves the accuracy of overlapping vegetable identification, but also provides a robust framework for enhancing precision agriculture.

This study examines Cambodian rice farmers’ willingness to pay for the weather-indexed insurance (WII) proposed to manage the financial impact of shifting monsoon rainfall patterns in Battambang Province in north-western Cambodia. Detailed interviews are conducted in the districts of Bavel and Thma Koul. We first analyse farmer respondents’ socioeconomic and demographic characteristics, climate change perceptions and experience, risk attitudes, and awareness of insurance. The binary logistic model is used to identify factors that significantly impact farmers’ willingness to pay (WTP) for WII. Our results show that farmers in general had lower awareness of how to use innovative financial products to adapt to extreme weather. The results also demonstrate that farmer respondents’ marital status, the number of off-farm labourers, and the farm size have a positive effect, whereas the number of children in the household has a negative effect on farmers’ WTP for WII. Specifically, being married, an increase of one off-farm labourer, and an increase of one hectare (ha) of farmland increase the probability of demand for WII by 38.6%, 21.4%, and 5.1%, respectively. In contrast, an increase of one child reduces the probability of WII demand by 9.7%. We also identify challenges confronted by Cambodian farmers for participating in the proposed WII scheme and provide relevant recommendations to overcome these challenges.

This study examines Cambodian farmers’ demand for weather index insurance (WII), an innovative financial product, for managing climate change-related risks. Rice and cassava farmers in Battambang Province of Cambodia were interviewed to understand their preferences for WII. We applied a binary logistic model to quantify the factors that influence farmers’ WII demand. We discovered that farmers’ marital status and off-farm labor are crucial factors that impact the demand for WII. More importantly, we also investigated gender differences, considering the critical role of women in the agricultural sector and personality differences between men and women. Our findings indicated that for male respondents, being married and having an additional off-farm laborer increase the probability of demand for WII by 72.6% and 36.8%, respectively. For female respondents, the education level is the most significant factor in making purchase decisions. An additional year of education increases the probability of WII demand by 5.0%. Generally, our results are consistent with some prior studies but inconsistent with others. This suggests that further research is necessary to understand the barriers associated with WII schemes and how to overcome them. Regardless, our study provides valuable insights for various stakeholders in implementing WII schemes, including financial professionals, insurance companies, communities, and governments, for designing more flexible WII products, improving farmers’ financial literacy, and providing effective post-event support to enhance farmers’ resilience to climate change.