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Developing countries are considered extremely vulnerable to climate change, due to their socioeconomic context (high levels of poverty) and high dependence of their livelihoods on natural resources. Rural areas in these countries concentrate most of the poorest and food-insecure people in the world, with farmers being among the most vulnerable to climate change. The impacts of climate change are expected to be spatially heterogeneous. In this sense, this paper aims at exploring the direct, marginal effect of climate change on farming system choice and its implications to food security in Mozambique, using a space-for-time approach. Our results suggest that major changes are to be expected in farming system choice and their spatial distribution due to climate change, which will potentially impact the livelihoods and food security status of smallholder farmers. Farming systems including food/cash crops and/or livestock, which are among the most food secure, will tend to be replaced by other systems in all climate scenarios. Mixed farming systems (including food and livestock) and livestock-oriented systems, mostly food insecure, predominant in arid areas are expected to expand with climate change. Food security and innovation stress maps were sketched out from the modelling results, identifying priority areas for public intervention. We also highlight how our approach can be an effective and easily replicable framework to address this type of issues in other developing regions facing similar problems.

This study aims to identify therelationship between the climatic variablesand yields of three crops (wheat (Triticum aestivum L.), maize (Zeamays L.) and cotton (Gossypiumhirsitum L.) in the Cukurova region ofTurkey. In the study, time series data wereused to analyse crop yields across variousclimate factors for the period 1975 to1999. The climatic variables were arrangedaccording to phonological periods of theexamined crops such as planting, floweringand harvesting time. A linear perturbationmodel (LPM) was used for the identificationof the role of climate variables. 27climatic factors were considered asexplanatory variables in the model. Astepwise selection method in the selectionand introduction of independent variableswas used for regression equation for wheat,maize and cotton. The results of the linearperturbation model (LPM) showed that theR2 values for wheat, maize and cottonwere found 46.1%, 57.2% and 74.5%,respectively. The highest variationcoefficient (CV) was found in maizeproduction (43.4%) followed by cotton(23.14%) and wheat (15.29%). The mostsignificant climatic factor affectingdeviations in crop yields is related intemperature at planting, flowering andharvesting time. It is suggested thatfarmers would be better off with theapplying adaptation measurements. Therefore, policy makers should focusefforts on reducing production risksproviding climatic information anddeveloping risk management institutions.

In the global effort to mitigate climate change, the parties of the United Nations Framework Convention on Climate Change (UNFCCC) are committed to producing annual reports on their national greenhouse gas (GHG) emissions. These reports are a valuable source of information. Among others, they can be used to measure the effectiveness of climate mitigation strategies over time. However, large parts of GHG inventories rely on estimated quantities and consequently, the reported figures are uncertain. Quantifying this uncertainty is crucial as it may affect our ability to distinguish the true trends from the intrinsic variability. In this study, five statistical techniques for uncertainty quantification, two of them being recommended by the Intergovernmental Panel on Climate Change (IPCC), were evaluated as to their ability to correctly estimate the variance. The standard Monte Carlo estimator, which is one of the two techniques recommended by the IPCC, tended to overestimate the true variance. It was no better than a naïve estimator. The propagation-based estimator, which is the other technique recommended by the IPCC, also tended to overestimate the true variance but to a lesser extent. Goodman’s estimator and a rescaled Monte Carlo estimator were both unbiased and consequently, they should be preferred when evaluating the performance of national climate mitigation policies.

There is growing interest in the useof forestry-offset projects to mitigate increasingconcentrations of carbon dioxide in the atmosphere. If forestry-offset projects are to be employed broadlyand successfully there need to be accounting rulesthat are easy to operationalize and effective inpreventing cheating. Since carbon is both tangibleand predictable in where it occurs it is feasible todevelop simple accounting rules. Such rules must beconservative with respect to the amount of carboncredited.If accounting practices based on the following simplerules are employed, costs will be kept low andprojects will credit only carbon that is physicallypresent: •Changes in living aboveground biomass must always be measured in forestry –offset projects. •Belowground living biomass can be estimated from aboveground living biomass in forestry-offset projects. Generalized root/shoot ratios can be used as long as conservative ratios are applied. •Not all changes in soil carbon stocks need to be measured, only those for which there is a possibility the stock is declining. •The necromass pool need not be measured except when there has been a recent disturbance (interval varies with ecosystem). • To insure that inaccurate techniques do not lead to overestimating of carbon stock changes, imprecise estimates of the carbon content of an ecosystem compartment should be discounted. •There should be no required level of accuracy associated with estimates of carbon stock changes in forestry-offset projects, but the creditable carbon should be discounted proportional to the uncertainty.

Worldwide, an increase in flood damage is observed. Governments are looking for effective ways to protect lives, buildings, and infrastructure. At the same time, a large investment gap seems to exist—a big difference between what should necessarily be done to curb the increase in damage and what is actually being done. Decision-makers involved in climate adaptation are facing fundamental (so-called deep) uncertainties. In the course of time, the scientific community has developed a wide range of different approaches for dealing with these uncertainties. One of these approaches, adaptation pathways, is gaining traction as a way of framing and informing climate adaptation. But research shows that “very little work has been done to evaluate the current use of adaptation pathways and its utility to practitioners and decision makers” (Lin et al. 2017, p. 387). With this paper, the authors, as action researchers and practitioners involved in two of the world’s largest real-life applications of this approach in flood risk management, aim to contribute to filling in that gap. Analysis of the experience in the United Kingdom and the Netherlands in long-term planning in flood risk management shows that the adaptation pathways approach is effective in keeping decision processes going forward, to the final approval of a long-term plan, and helps increase awareness about uncertainties. It contributes to political support for keeping long-term options open and motivates decision-makers to modify their plans to better accommodate future conditions. When it comes to implementing the plans, there are still some major challenges, yet to be addressed, amongst others: the timely detection of tipping points in situations with large natural variability, the inclusion of measures that prepare for a switch to transformational strategies, and the retention of commitment of regional and local authorities, non government organizations, and the private sector, to climate adaptation as national policies move from blueprint planning to adaptive plans. In delivering this feedback, the authors hope to motivate the scientific community to take on these challenges.

A major challenge confronting the scientific community is to understand both patterns of and controls over spatial and temporal variability of carbon exchange between boreal forest ecosystems and the atmosphere. An understanding of the sources of variability of carbon processes at fine scales and how these contribute to uncertainties in estimating carbon fluxes is relevant to representing these processes at coarse scales. To explore some of the challenges and uncertainties in estimating carbon fluxes at fine to coarse scales, we conducted a modeling analysis of canopy foliar maintenance respiration for black spruce ecosystems of Alaska by scaling empirical hourly models of foliar maintenance respiration (Rm) to estimate canopy foliar Rm for individual stands. We used variation in foliar N concentration among stands to develop hourly stand-specific models and then developed an hourly pooled model. An uncertainty analysis identified that the most important parameter affecting estimates of canopy foliar Rm was one that describes Rm at 0 ∘C per g N, which explained more than 55% of variance in annual estimates of canopy foliar Rm. The comparison of simulated annual canopy foliar Rm identified significant differences between stand-specific and pooled models for each stand. This result indicates that control over foliar N concentration should be considered in models that estimate canopy foliar Rm of black spruce stands across the landscape. In this study, we also temporally scaled the hourly stand-level models to estimate canopy foliar Rm of black spruce stands using mean monthly temperature data. Comparisons of monthly Rm between the hourly and monthly versions of the models indicated that there was very little difference between the estimates of hourly and monthly models, suggesting that hourly models can be aggregated to use monthly input data with little loss of precision. We conclude that uncertainties in the use of a coarse-scale model for estimating canopy foliar Rm at regional scales depend on uncertainties in representing needle-level respiration and on uncertainties in representing the spatial variability of canopy foliar N across a region. The development of spatial data sets of canopy foliar N represents a major challenge in estimating canopy foliar maintenance respiration at regional scales.

We quantitatively evaluated mitigation potentials of greenhouse gas emissions from agriculture and land use sectors in Indonesia. We also determined for the land use sector 1) how much time is needed to plan the mitigation strategy, and 2) how much time is needed to evaluate the effectiveness of mitigation measures? We used a bottom-up type model on emission mitigation named Agriculture Forestry and Other Land Use Bottom-up model (AFOLU-B model) to consider a characteristics time sequence of actions, effects and costs of mitigation countermeasures. To input to the model, we developed future scenarios on human activities in these sectors based on data from Indonesian government. We found that i) evaluation on effects and costs of mitigation measures from a long-term viewpoint and their continuing implementation are required to increase future mitigation potentials under a limited budget. ii) a long-term reduction target will also play an important role for cost-effective countermeasure selection. iii) reforestation of slow glowing species and enhanced natural regeneration will cause great increase in mitigation potential in the country.

China is experiencing rapid population aging and environmental pollution problems. There is a strong body of evidence supporting the notion that population aging may increase energy demand and aggravate related emissions. In this paper, we construct an extended KAYA model with aging variables. Drawing on panel data of 82 cities in China from 2014 to 2017, we aim to investigate whether population aging affects air pollution. Our estimates show that population aging has a significant negative impact on air quality, whereby a 1% change in the proportion of the resident population aged 65 years or above is associated with a 0.121% change in Air Quality Index in the same direction. The implication is that the increase in population aging aggravates air pollution in China in our study periods. By introducing a mediation effect, we further analyze the possible pathways of population aging with regard to air quality. The results show that population aging aggravates air pollution by increasing fossil fuel consumption and increasing the consumption of medical products. Promoting the development of energy-efficient products and services related to elderly people’s consumption may be necessary to mitigate the environmental impact of population aging in China.

Hầu hết các nghiên cứu mô hình hóa khám phá các kịch bản giảm thiểu khí nhà kính lâu dài tập trung vào các con đường phát thải tiết kiệm chi phí hướng tới một mục tiêu khí hậu nhất định, như mục tiêu khí hậu được quốc tế công nhận là thực hiện duy trì sự gia tăng nhiệt độ toàn cầu dưới 2°C so với mức trước công nghiệp (mục tiêu 2°C). Tuy nhiên, thời điểm khác nhau của việc giảm thiểu dẫn đến sự gia tăng tạm thời nhiệt độ khác nhau trong suốt thế kỷ và sau đó dẫn đến sự khác biệt trong hồ sơ thời gian không chỉ của chi phí giảm thiểu mà còn cả chi phí thích ứng và thiệt hại do biến đổi khí hậu còn lại. Nghiên cứu này bổ sung vào tài liệu hiện có bằng cách tập trung vào các hệ quả của những sự khác biệt này đối với việc đánh giá một bộ ba kịch bản giảm thiểu (hành động sớm, hành động dần dần và hành động trì hoãn), cả ba đều giới hạn sự gia tăng nhiệt độ toàn cầu dưới 2°C so với mức trước công nghiệp, sử dụng các tỷ lệ chiết khấu khác nhau. Nghiên cứu cho thấy rằng con đường giảm thiểu dần dần là, đối với các tỷ lệ chiết khấu này, được ưa chuộng hơn so với hành động sớm hoặc trì hoãn về tổng chi phí khí hậu và lợi ích ròng. Ngược lại, các chi phí và lợi ích tương đối của các kịch bản hành động giảm thiểu sớm hoặc trì hoãn lại phụ thuộc mạnh vào tỷ lệ chiết khấu được áp dụng. Đối với các tỷ lệ chiết khấu cụ thể, những con đường này do đó có thể được ưa chuộng vì những lý do khác, chẳng hạn như giảm thiểu sự không chắc chắn lâu dài trong chi phí khí hậu thông qua hành động sớm.

The climatic and non-climatic stresses impacted adversely to the functioning and productivity of the forests, resulting in disturbing the existing carbon flow in the atmosphere. Cedrus deodara occurs in pure forest stands throughout the Western Himalayas and has high biomass and soil carbon sequestration potential. The present study aims to provide the contribution of the Cedrus deodara forests under the current stresses for climate mitigation by analysing the three elevation ranges of the Dhanaulti forest division of Garhwal Himalaya, India. The results report that soil organic carbon (SOC) was adversely and bulk density favourably related with elevation. Moreover, SOC as CO2eq also decreased significantly with an increase in soil depths. Biomass carbon for various parts of the plant was also estimated for the three elevations of the Cedrus deodara forests. The trend in total carbon stock (bole, branch, twig, foliage and soil) decreased significantly with an increase in altitude. The carbon stock of Cedrus deodara forests was maximum (545 t ha−1) at upper altitude (2350 m.a.s.l) and minimum (330 t ha−1) at a lower altitude (2050 m.a.s.l). The difference in litter production between the seasons is significant with maximum production in summer followed by rainy and winter seasons. This study provides inputs for greenhouse gas (GHG) estimation for national communication to various platforms. The information on the soil is crucial for understanding about the ecology of the forests assisting prediction of functioning and productivity of forests.