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In the current era, entire global economies are transitioning to sustainable development because of global warming and climate change. Due to turbulence in environmental issues such as weather shocks, climate change and basic infrastructure and industrial planning, many countries are changing their approach and taking green steps. This paper assesses sustainable finance in India and the ways in which India can mitigate climate changing risk toward zero carbon policy and supporting SDGs 2030. It talks about the physical and transitional climate risks in India like surface temperature, heat wave concourse, etc., and provides a comprehensive analysis of how the corporate sector is imparting its role in sustainable nature through corporate social responsibility (CSR). India is an emerging economy where energy is an essential component. This study analyzes about supply of energy and how India is shifting from traditional energy sources to renewal energy sources.

Thus, the objective of this study is to focus on the initiatives taken by the Indian government for sustainable finance through green bond policy at national and international platforms using the Panchamrit framework adopted by India thereby focusing on India’s sustainable policy support for SDGs 2030. Also, this research proposes numerous recommendations for future sustainable finance research in the context to India, which includes developing and diffusing innovative sustainable financing instruments, magnifying, and managing the profitability and returns of sustainable financing, making sustainable finance more sustainable, and leveraging the power of new-age technologies for sustainable finance.

Malaria parasites are strongly dependent on Anopheles mosquitoes for transmission; for this reason, mosquito population dynamics are a crucial determinant of malaria risk. However, temperature and rainfall play a significant role in both aquatic and adult stages of the Anopheles. Consequently, it is important to understand the biology of malaria vector mosquitoes in the study of malaria transmission. In this study, we develop a climate-based, ordinary-differential-equation model to analyze how rainfall and temperature determine mosquito population size. In the model, we consider in detail the influence of ambient temperature on gonotrophic and sporogonic cycles over Amajuba District, Kwazulu-Natal Province, South Africa. In particular, we further use the model to simulate the spatial distribution of the mosquito biting rate over the study region. Our results reflect high seasonality of the population of An. gambiae over the region and also demonstrate the influence of climatic factors on the mosquito population dynamics.

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.

About 140 countries have announced or are considering net zero targets. To explore the implications of such targets, we apply an integrated earth system–economic model to investigate illustrative net zero emissions scenarios. Given the technologies as characterized in our modeling framework, we find that with net zero targets afforestation in earlier years and biomass energy with carbon capture and storage (BECCS) technology in later years are important negative emissions technologies, allowing continued emissions from hard-to-reduce sectors and sources. With the entire world achieving net zero by 2050 a very rapid scale-up of BECCS is required, increasing mitigation costs through mid-century substantially, compared with a scenario where some countries achieve net zero by 2050 while others continue some emissions in the latter half of the century. The scenarios slightly overshoot 1.5^∘C at mid-century but are at or below 1.5^∘C by 2100 with median climate response. Accounting for climate uncertainty, global achievement of net zero by 2050 essentially guarantees that the 1.5^∘C target will be achieved, compared to having a 50–50 chance in the scenario without net zero. This indicates a tradeoff between policy costs and likelihood of achieving 1.5^∘C.

Detrended fluctuation analysis (DFA) is used to investigate correlations between the monthly average of the maximum daily temperatures for different locations in the continental United States and the different climates these locations have. When we plot the scaling exponents obtained from the DFA versus the standard deviation of the temperature fluctuations, we observe crowding of data points belonging to the same climates. Thus, we conclude that by observing the long-time trends in the fluctuations of temperature it would be possible to distinguish between different climates.

The climate sensitivity of electricity demand in India is likely to be highly sensitive to growth in income. Thus, both intensive and extensive adjustments in cooling and heating will play an important role in determining future climate change impacts on electricity demand. This paper utilizes a national level panel dataset of 28 Indian states for the period 2005–2009. The preferred estimates indicate that climate change will increase electricity demand by 6.7% with 4% p.a. GDP growth and 8.5% with 6% p.a. GDP growth in 2030 over the reference scenario of no climate change. This reflects the fact that the estimated marginal effect of a hotter climate is greater when income is higher. Over 50% of the climate change impacts will be due to extensive adjustments as the current penetration of space conditioning equipments such as air conditioners is very low.

Future carbon dioxide (CO₂) emissions under a carbon tax depend on the time-path of the economy under baseline (business-as-usual) conditions as well as the extent to which the policy reduces emissions relative to the baseline. Considerable uncertainties surround the baseline forecasts for fuel prices, energy efficiency (energy-GDP ratios), and GDP, as evidenced by the significant ranges in the forecasts by government agencies and research institutions in the U.S. This paper assesses the significance of these uncertainties to the path of CO₂ emissions under a carbon tax. We do this by examining the emissions levels and quantities of abatement that result from the E3 general equilibrium model under a range of alternative baseline forecasts for fuel prices, energy efficiency, and GDP, where the different baselines are produced through suitable changes to key model parameters. In addition, we consider how the time-profile of the carbon tax needed to achieve specified CO₂ abatement targets is affected by such forecast-linked changes in parameters.

We find that the sensitivity of baseline emissions to alternative forecasts depends on the particular forecasted variable under consideration. Baseline CO₂ emissions are highly sensitive to alternative scenarios related to the rate of energy efficiency improvements in the nonenergy sector and the rate of general economic growth. In contrast, such emissions are much less sensitive to alternative scenarios related to the productivity of fossil fuel production. The extent of abatement from the baseline is generally fairly insensitive to changes in the scenarios for time-paths of fuel prices, energy-efficiency and GDP. We also find that short-term emissions targets can be achieved with relatively moderate carbon taxes under all of the baseline scenarios considered.

The United States is currently on pace to fall well short of its promises to reduce greenhouse gas emissions by 26–28%, relative to 2005, by 2025, under the UN Framework and Convention on Climate Change (UNFCCC) Paris Agreement, even if President Trump did not eliminate most Obama-era climate regulations. However, there still exists interest in reducing emissions, especially from some members of Congress, and there are a number of federal policy options to reduce greenhouse gas emissions if Congress (or a new administration in 2021) so chooses. In this paper, we show that a federal economy-wide carbon tax on US carbon dioxide emissions could significantly contribute to the reductions necessary to fulfill the US international climate commitments. Using a detailed multi-sector computable general equilibrium (CGE) model, we predict the carbon price paths that would be necessary to meet the 28% emissions target and show the economic costs of such carbon-pricing policies. We then demonstrate how both the price paths and associated costs change if action is delayed.

A series of recent extreme weather events — from hurricanes and wildfires in the U.S. to heat waves in Europe and floods in Japan — have put a spotlight on climate-related risks. Yet the implications for investment portfolios — stemming from a rising frequency and intensity of such events — have been notoriously hard for investors to grasp…

Innovation is an ever-increasing focus for modern organizations; yet research studies on organizational culture have tended to neglect this aspect. This paper specifies the key factors characterising a company's innovation culture and examines the top managers' executed impact as organizational leaders from the individual perspective on implementing and fostering it in their respective organizations. Based on a conceptual framework, the empirically identified key factors of the (1) formal embedment, (2) climate, (3) incentives and reward allocation, (4) integration into decision process, (5) cross-hierarchical communication and (6) communication style are presented. Within the empirical study, 37 top managers of 21 leading companies in the industries of “fashion and accessories” and “watch and jewelry” were interviewed. The results indicate that the impact of top managers lag behind their potential to advance innovativeness through innovation culture, and the detected deficit represents scope for improvement. The paper concludes by highlighting the implications of the study and its limitations.

Accounting for socioeconomic and demographic variables, as well as country-specific effects, households' marginal willingness to pay for climate is revealed using European data on life-satisfaction. Individuals located in areas with lower average levels of sunshine and higher average levels of relative humidity are less satisfied as are individuals in locations subject to significant seasonal variation in monthly mean temperatures and rain days. Ranking regions by climate households appear strongly to favor the Mediterranean climate over the climate of Northern Europe.

Following the UK’s hosting of the United Nations Convention of the Parties Climate Summit in 2021, political targets for reducing greenhouse gas emissions — “Net-Zero” — have gained momentum. We address the gap in how public preferences are accounted for in climate decision-making by applying Contingent-Valuation techniques which ask people to state their Willingness-to-Pay (WTP) for the UK’s 2050 Net-Zero target. Mean WTP is £37.57/household to support Net-Zero (median £11.25), with a present-value of £2.3 billion across UK households. While younger people are more likely to experience the long-term impacts of climate change, older generations are willing to pay more to support it, suggesting that public support for Net-Zero is largely based on “nonuse” benefits, rather than direct “use” benefits to oneself. The COVID-19 epidemic affected WTP bids in a quarter of respondents. Finally, we explore how choice of positive or normative discount rate affects policy conclusions when monetizing consumer preferences.

In this paper, I advance and empirically support the indigenous religious values hypothesis, which holds that religions espouse values indigenous to the countries in which they developed. To identify the indigenous values of a religion’s homeland, I rely on the negative relationship between individualism and rainfall variation. I find strong empirical support for the hypothesis that contemporary individualism depends on rainfall variation in the homelands of religions to which a country’s population adheres. Indeed, this relationship explains over a quarter of the international variation in individualism. This effect is robust to controls for the role of religion in institutional and technological transfers and the confluence of conversion and colonisation. In keeping with the explicitly religious nature of the mechanism proposed here, I also find that rainfall variation in religion homelands plays a greater role in explaining the values of countries with greater religious freedom and the values of individuals who are more religious or members of religious minorities.

The role of vegetation cover within the processes that link land and atmosphere is of stringent interest for the correct modeling of Climate dynamics. Temporal and spatial correlation of the terrestrial coverage varies according to Climate and acts as a major forcing on it through changes in surface energy and water balance as well as in the carbon cycle. Recent studies have enhanced the actual and potential impact of this forcing on the radiative balance thus evidencing effects that are at least comparable to that due to all the anthropogenic greenhouse gases together. At now, observational studies on land cover dynamics are strongly in progress thanks to satellite data. The availability of continuous observations of the land surface can allow us to understand the correlation structure, both in time and in space, that characterizes the land cover activity. Satellites provide time series of photosynthetic activity measures that can be regarded as a succession of observations of a two-dimensional scalar field. We exploited the paradigm of fluctuating surfaces as a mechanic analogue for our problem. To capture vegetation cover characteristic time-scales, persistence properties were evaluated by analysing annual maps of NDVI-AVHRR time series and persistence probability was estimated by using the sing-time distribution methodology. The analysis performed for ecoregions of Italian and Greek territories evidenced signatures of short range persistence with characteristic time scales that depend on land cover, climate, and anthropic activities. Our results confirm that such an approach can provide a useful parameterisation for including vegetation into climate models as a dynamical component.

Atmospheric and oceanic climate factors and conditions play a crucial role in modulating seasonal/annual tropical cyclone activity in the North Atlantic Ocean Basin. In the following, correlations between North Atlantic tropical cyclone activity including frequency of occurrence and pathways are explored, with special emphasis on hurricanes. The value of two-dimensional and three-dimensional data sets representing climate patterns is investigated. Finally, the diagnostic study of historical tropical cyclone and hurricane temporal and spatial variability and relationships to climate factors lead to a statistical prognostic forecast, made in April, 2010, of the 2010 tropical cyclone and hurricane season. This forecast is tested both retrospectively and presently and is shown to be quite accurate. Knowing the probability of the frequency of occurrence, i.e. the numbers of named storms to form in general and the number of hurricanes (NHs) that are likely to form, is important for many societal sectors. However, the reliable forecasts of probable pathways of predicted events, specifically the likely NH land falls along the coastlines of the United States, should have great potential value to emergency planners, the insurance industry, and the public. The forecast provided in this study makes such a prognostication. As the 2010 hurricane season has progressed, an update of the goodness of the forecast is shown to be quite accurate in numbers of named events, hurricanes, major hurricanes (MHs), and landfalls. The mathematical and statistical methodology used in this study, which could be coupled to next generation "empirical modal decomposition," suggests that this may signal a new era in the future of tropical cyclone forecasting, including the reliable prognostication of numbers of events, intensities of events, and the pathways of those events. The ability to reliably predict the probability and location of land falls of these destructive events would be very powerful indeed.

In the recent United Nations Framework Convention on Climate Change (UNFCCC) negotiations, sectoral trading was proposed to encourage early action and spur investment in low carbon technologies in developing countries. This mechanism involves including a sector from one or more nations in an international cap-and-trade system. We analyze trade in carbon permits between the Chinese electricity sector and a US economy-wide cap-and-trade program using the MIT Emissions Prediction and Policy Analysis (EPPA) model. In 2030, the US purchases permits valued at $42 billion from China, which represents 46% of its capped emissions. In China, sectoral trading increases the price of electricity and reduces aggregate electricity generation, especially from coal. However, sectoral trading induces only moderate increases in generation from nuclear and renewables. We also observe increases in emission from other sectors. In the US, the availability of cheap emissions permits reduces the cost of climate policy and increases electricity generation.

Inside a greenhouse, during the day, the temperature rises very quickly, while the plants have to face temperatures that rise to more than 35${}^{\circ }$C. The plant closes its pores to limit sweating and stops growing. As soon as it gets hot, it is therefore necessary to ventilate the greenhouse. In this context, this research aims to investigate the behavior of the natural ventilation on the internal climate of the tunnel greenhouse, which contains two openings in the roof. The effect of the position of the openings on heat transfer is considered, thus promoting photosynthesis and plant growth. The vorticity transport equation, the Poisson equation and the energy equation are discretized by using the finite volume method. Two-dimensional simulations that described laminar flows in a steady state were carried out. Flows are studied for a range of parameters: the Rayleigh number, Ra, $10^3\le \mathrm{\text{Ra}}\le 10^6$, and three positions of opening ventilation. The results reveal that the ventilation through the top opening position allows the best creation of heat exchanges between the air inside the greenhouse and its atmosphere, which serves to conserve the plant under a favorable climate that allows its growth.

Extreme events often bring unexpected situations and impacts, as the sequence of hurricanes and other natural disasters in summer and fall 2017 demonstrated. To reduce the risks associated with such events, many have focused on reducing uncertainty in prediction or reducing vulnerability. Although both are worthy goals, we suggest that the research community should also be focusing on the nature of surprise itself, to investigate the role of surprise in extreme events and its implications. Surprise arises when reality differs from people’s expectations. Multiple factors contribute to creating surprise, including the dynamic nature of natural and human systems, the limitations of scientific knowledge and prediction, and the ways that people interpret and manage risks, not to mention climate variability and change. We argue that surprise is an unavoidable component of weather and climate disasters — one that we must acknowledge, learn to anticipate, and incorporate into risk assessment and management efforts. In sum, although it may seem paradoxical, we should be learning how to expect surprise.

Although agricultural production contributed about 10% of all greenhouse gas emissions in the United States in 2019, existing agricultural practices are capable of making the sector carbon neutral. Whether American agriculture will ultimately achieve carbon neutrality is ultimately a question of political will, not a scientific one. Given the right policy environment, farms and ranches will be able to cut their emissions and use their land to sequester carbon, while becoming more climate resilient, productive, and profitable…

Urban Design Climate Workshops (UDCW) are underway to focus on urban heat stress adaptation integrated with flooding resiliency and greenhouse gas emission mitigation. Integrated Climate Mitigation and Climate Adaptation prioritizes mitigation strategies that yield concurrent adaptive benefits over those that do not. On the one hand, dense, compact urban forms that mix land use and support mass transit reduce the carbon footprint. On the other hand, these dense urban districts can be configured to reduce the impact of urban heat and storms due to the changing climate while enhancing quality of life.