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Recent studies confirm that weather affects the Chinese stock markets, based on a linear model. This paper revisits this topic using DCCA cross-correlation coefficient (ρ_DCCA(n)), which is a nonlinear method, to determine if weather variables (i.e., temperature, humidity, wind and sunshine duration) affect the returns/volatilities of the Shanghai and Shenzhen stock markets. We propose an asymmetric ρ_DCCA(n) by improving the traditional ρ_DCCA(n) to determine if different cross-correlated properties exist when one time series trending is either positive or negative. Further, we improve a statistical test for the asymmetric ρ_DCCA(n). We find that cross-correlation exists between weather variables and the stock markets on certain time scales and that the cross-correlation is asymmetric. We also analyze the cross-correlation at different intervals; that is, the relationship between weather variables and the stock markets at different intervals is not always the same as the relationship on the whole.

This study investigates the correlation between weather and agricultural futures markets on the basis of detrended cross-correlation analysis (DCCA) cross-correlation coefficients and $q$-dependent cross-correlation coefficients. In addition, detrended fluctuation analysis (DFA) is used to measure extreme weather and thus analyze further the effect of this condition on agricultural futures markets. Cross-correlation exists between weather and agricultural futures markets on certain time scales. There are some correlations between temperature and soybean return associated with medium amplitudes. Under extreme weather conditions, weather exerts different influences on different agricultural products; for instance, soybean return is greatly influenced by temperature, and weather variables exhibit no effect on corn return. Based on the detrending moving-average cross-correlation analysis (DMCA) coefficient and DFA regression results are similar to that of DCCA coefficient.

Climate change will push the weather experienced by people affected outside the bounds of historic norms, resulting in unprecedented weather events. But people and firms should be able to learn from their experience of unusual weather and adjust their expectations about the climate distribution accordingly. The efficiency of this learning process gives an upper bound on the rate at which adaptation can occur and is therefore important in determining the adjustment costs associated with climate change. Learning about climate change requires people to infer the state of a changing probability distribution (climate) given annual draws from that distribution (weather). If the climate is stationary, it can be inferred from the distribution of historic weather observations, but if it is changing, the inference problem is more challenging. This paper first develops different learning models, including an efficient hierarchical Bayesian model in which the observer learns whether the climate is changing and, if it is, the functional form that describes that change. I contrast this with a less efficient but simpler learning model in which observers react to past changes but are unable to anticipate future changes. I propose a general metric of learning costs based on the average, discounted squared difference between beliefs and the true climate state and use climate model output to calculate this metric for two emissions scenarios, finding substantial relative differences between learning models and scenarios but small absolute values. Geographic differences arise from spatial patterns of warming rates and natural weather variability (noise). Finally, I present results from an experimental game simulating the adaptation decision, which suggests that people are able to learn about a trending climate and respond proactively.

This study investigates the effects of combinations of climate smart agricultural practices on risk exposure and cost of risk. We do this by examining the different risk components — mean, variance, skewness, and kurtosis — in a multinomial treatment effects framework by controlling weather variables for key stages of crop growth. We found that adoption of combinations of practices is widely viewed as a risk-reducing insurance strategy that can increase farmers’ resilience to production risk. The hypothesis of equality of weather parameters across crop development stages is also rejected. The heterogeneous effects of weather across crop growth stages have important implications for climate change adaptation to maximize quasi-option value. For a country that has the vision to build a climate-resilient economy, this knowledge is valuable to identify a combination of climate smart practices that minimizes production risk under variable weather conditions.

Globalization, commercialization, modernization, erratic climatic conditions, individual expectations, contagion, and government policies are some of the reasons attributed to farmers’ suicides. This study hypothesizes that farmer suicides in India are primarily linked to loss in agricultural productivity which in turn is affected by adverse weather and low penetration of irrigation networks. Using panel data of 16 major states in India, from 1996 to 2015 and Control Function (CF) approach, the study shows that keeping all other factors fixed, a one degree rise in temperature results in 4.8% higher farmer suicides through a 3.6% decline in agricultural productivity. Further, the study highlights the significant role played by the contagion factors influencing farmer suicides. The study argues for policy responses that address covariate shocks arising from weather vagaries, price volatility, and liquidity constraint as well as idiosyncratic shocks arising from farmer-specific characteristics.

A framework for creating thermal zones in a building for effective and efficient heating ventilation and air conditioning (HVAC) system design is introduced here. This method is based on simple “sort and eliminate” schemes and requires design cooling loads of conditioned spaces obtained from load calculation tools as primary input. The developed methodology is applied for creating thermal zones, determining corresponding supply conditions and ascertaining sizing of a dedicated outdoor air system (DOAS) with local recirculating units. A simulation study on a prototype-building model shows that a DOAS coupled with zoned recirculating systems that serve distinct thermal zones in a building (zoned model) perform comparatively better in controlling both space temperatures and humidity without significantly compromising HVAC energy and chiller loads than un-zoned HVAC systems serving the whole building as a single thermal block (un-zoned model). The consistency in the performance of zoned HVAC systems is verified by applying three different simulation weather files for New Delhi. Better performance along with logical and computational simplicity makes this design procedure a good alternative to traditional methodologies.

Wind turbines and Solar Photovoltaic (PV) electric energy generators are the lowest cost producers of electricity available in 2019, costing about half as much as a new coal plant’s electric energy (Lazard’s Levelized Cost of Energy Analysis, 2018). They have the important characteristic of delivering electricity without releasing greenhouse gases like carbon dioxide (CO₂). Yet, almost all experts and plans for the future say that wind and solar will not be dominant contributors to electricity generation for the next couple of decades. They say we must continue fossil fuels with their associated greenhouse gases because of problems with renewable generators…

This paper inquiry into Youshui hydrology under intersect influence of periodical weather variety and earthquakes, seek regularity of weather and earthquake and get efficient prevention and do good for their estimation, give substantial and national minority literature research by solid example usage as regional evidence and certification combine with wavelet analysis and earthquake theory, geology and dynamics, benefit to social national minority humanities making, all these have innovation.