Transitional land uses produced through urbanisation continue to change the landscape and fragment ecological structures including green spaces across Europe (Nilsson et al., 2013). Green spaces offer significant benefits to humans, contributing to wellbeing and life satisfaction (Taylor, 2002). The understanding of how these unique green spaces spaces function and provide benefits to humans, and how landscape change in peri-urban contexts affects their performance, is important. The scope of this research is to contribute to an understanding of landscape fragmentation within some of Europe's polycentric urban regions, their peri-urban green spaces, and the associated impacts upon human quality of life. Two urban regional case studies, Paisley near Glasgow, Scotland, and Vantaa, near Helsinki, Finland were analysed and compared. The results indicate that humans interacting with more physically or ecologically fragmented peri-urban green spaces have higher self-reported life satisfaction levels. Though no statistically significant characteristics were apparent between life satisfaction and fragmented green space characteristics, this research was able to identify those specific structural attributes and physical characteristics of interstitial peri-urban green spaces within a polycentric region in a fragmented state that contribute to the physical, social, and psychological aspects of human wellbeing. The statistically significant eco-spatial characteristics of polycentric peri-urban interstitial green spaces that are reported to impact human wellbeing are the size, proximity, maintenance and management, and the level of greenness within its vegetation composition and setting. Overall, a spatially diverse, fragmented, peri-urban landscape whose green spaces are extensively sized, naturalistically shaped with horizontal vegetation and normal sized edges, most often parks or woodlands or forests which are integrated and physically connected to another green space which is moderately clean and somewhat safe as well as being located close to or adjacent to a heavy-trafficked road provide the most human wellbeing benefits.

Le contrôle de l'évolution des logiciels exige une compréhension profonde des changements et leur impact sur les différents artefacts du système. Nous proposons une approche de multi-modélisation pour l'analyse d'impact du changement pour une compréhension des effets des modifications prévus ou réels dans les systèmes distribués. Ce travail consiste à élaborer une modélisation des artefacts logiciels et de leur différents liens d'interdépendance pour construire un système à base de connaissance permettant, entre autres, d'assister les développeurs et les chargés de l'évolution des logiciels pour étblir une évaluation a priori de l'impact des modifications.La modélisation que nous élaborons intègre deux descriptions majeures des logiciels, dans un premier temps, la description structurelle sous-jacente qui englobe l'ensemble des niveaux granulaires et l'abstraction des constituants logiciels, et ensuite la description qualitative conçue pour s'intégrer à la description précédente. Deux modèles, d'abord élaborés individuellement pour les deux descriptions respectives, ont été intégrés ou mis en correspondance dans l'objectif d'étudier l'impact de toute modification et sa potentielle propagation à travers les constituants logiciels concernés. Lors de chaque modification, il devient alors possible d'établir un bilan qualitatif de son impact. La modélisation intégrée est élaborée pour se prêter à un raisonnement à base de règles expertes. La modélisation proposée est en cours d'expérimentation et validation à travers le développement d'une plate-forme d'implémentation basée sur l'environnement Eclipse. / The software evolution control requires a complete understanding of the changes and their impact on the various systems artifacts. We propose a multi-modeling approach for the change impact analysis to provide assistance in understanding the effects of projected or actual changes in distributed software systems. This work elaborate the modeling of software artifacts along with their various interdependencies to build a knowledge-based system, which allows, among others, an assistance for the software developers or maintenance engineers to establish an a priori evaluation of impact of changes. The model we develop integrates two major descriptions of software, at first, the underlying structural description that encompasses the levels of granularity and abstraction of software artifacts, and then the qualitative description designed to integrate the structural description. Initially, the formal models are designed separately for the respective descriptions, and then these are integrated for the objective to study the change impact and its potential propagation through the affected software artifacts. For a change, it is important to establish a qualitative assessment of its impact. The integrated modeling leads to a reasoning based on expert rules. The proposed model is being tested and validated through the development of a platform, implemented in the Eclipse environment.

<p>Economics is about the management of scare resources. In agricultural production, water stress and excess heat are the main constraints. The three essays of this dissertation try to improve our understandings of how climate and water resources interact with agricultural markets, and how global changes in agricultural markets may affect water resources. I construct empirical and simulation models to explain the interplay between agriculture and water. These models integrate economic theories with environmental sciences to analyze the hydroclimatic and economic information at different geospatial scales in a changing climate. </p> <p>In the first essay, I illustrate how irrigation, as a potential adaptation channel, can reduce the volatility of crop yields and year-on-year variations caused by the projected heat stress. This work includes estimation of yield response to climate variation for irrigated and rainfed crops; and global projections of change in the mean and the variation of crop yields. I use my estimated response function to project future yield variations using NASA NEX-GDDP climate data. I show that the impact of heat stress on rainfed corn is around twice as big as irrigated practices. </p> <p>In the second essay, I establish a framework for estimating the value of soil moisture for rainfed production. This framework is an extension of Schlenker and Roberts (2009) model enabled by the detailed soil moisture information available from the Water Balance Model (WBM). An important contribution is the introduction of a cumulative yield production function considering the daily interaction of heat and soil moisture. I use this framework to investigate the impacts of soil moisture on corn yields in the United States. However, this framework can be used for the valuation of other ecosystem services at daily basis.</p> <p>In the third essay, I have constructed a model that explains how the global market economy interacts with local land and water resources. This helps us to broaden the scope of global to local analysis of systems sustainability. I have employed SIMPLE-G-W (a Simplified International Model of agricultural Prices, Land use, and the Environment- Gridded Water version) to explain the reallocation across regions. The model is based on a cost minimization behavior for irrigation technology choice for around 75,000 grid cells in the United States constrained by water rights, water availability, and quasi-irreversibility of groundwater supply. This model is used to examine the vulnerability of US land and water resources from global changes.</p>

Nous proposons une approche probabiliste afin de déterminer l’impact des changements dans les programmes à objets. Cette approche sert à prédire, pour un changement donné dans une classe du système, l’ensemble des autres classes potentiellement affectées par ce changement. Cette prédiction est donnée sous la forme d’une probabilité qui dépend d’une part, des interactions entre les classes exprimées en termes de nombre d’invocations et d’autre part, des relations extraites à partir du code source. Ces relations sont extraites automatiquement par rétro-ingénierie. Pour la mise en oeuvre de notre approche, nous proposons une approche basée sur les réseaux bayésiens. Après une phase d’apprentissage, ces réseaux prédisent l’ensemble des classes affectées par un changement. L’approche probabiliste proposée est évaluée avec deux scénarios distincts mettant en oeuvre plusieurs types de changements effectués sur différents systèmes. Pour les systèmes qui possèdent des données historiques, l’apprentissage a été réalisé à partir des anciennes versions. Pour les systèmes dont on ne possède pas assez de données relatives aux changements de ses versions antécédentes, l’apprentissage a été réalisé à l’aide des données extraites d’autres systèmes. / We study the possibility of predicting the impact of changes in object-oriented code using bayesian networks. For each change type, we produce a bayesian network that determines the probability that a class is impacted given that another class is changed. Each network takes as input a set of possible relationships between classes. We train our networks using historical data. The proposed impact-prediction approach is evaluated with two different scenarios, various types of changes, and five systems. In the first scenario, we use as training data, the changes performed in the previous versions of the same system. In the second scenario training data is borrowed from systems that are different from the changed one. Our evaluation showed that, in both cases, we obtain very good predictions, even though they are better in the first scenario.

La maintenance du logiciel est une phase très importante du cycle de vie de celui-ci. Après les phases de développement et de déploiement, c’est celle qui dure le plus longtemps et qui accapare la majorité des coûts de l'industrie. Ces coûts sont dus en grande partie à la difficulté d’effectuer des changements dans le logiciel ainsi que de contenir les effets de ces changements. Dans cette perspective, de nombreux travaux ont ciblé l’analyse/prédiction de l’impact des changements sur les logiciels. Les approches existantes nécessitent de nombreuses informations en entrée qui sont difficiles à obtenir. Dans ce mémoire, nous utilisons une approche probabiliste. Des classificateurs bayésiens sont entraînés avec des données historiques sur les changements. Ils considèrent les relations entre les éléments (entrées) et les dépendances entre changements historiques (sorties). Plus spécifiquement, un changement complexe est divisé en des changements élémentaires. Pour chaque type de changement élémentaire, nous créons un classificateur bayésien. Pour prédire l’impact d’un changement complexe décomposé en changements élémentaires, les décisions individuelles des classificateurs sont combinées selon diverses stratégies. Notre hypothèse de travail est que notre approche peut être utilisée selon deux scénarios. Dans le premier scénario, les données d’apprentissage sont extraites des anciennes versions du logiciel sur lequel nous voulons analyser l’impact de changements. Dans le second scénario, les données d’apprentissage proviennent d’autres logiciels. Ce second scénario est intéressant, car il permet d’appliquer notre approche à des logiciels qui ne disposent pas d’historiques de changements. Nous avons réussi à prédire correctement les impacts des changements élémentaires. Les résultats ont montré que l’utilisation des classificateurs conceptuels donne les meilleurs résultats. Pour ce qui est de la prédiction des changements complexes, les méthodes de combinaison "Voting" et OR sont préférables pour prédire l’impact quand le nombre de changements à analyser est grand. En revanche, quand ce nombre est limité, l’utilisation de la méthode Noisy-Or ou de sa version modifiée est recommandée. / Software maintenance is one of the most important phases in the software life cycle. After the development and deployment phases, maintenance is a continuous phase that lasts until removing the software from operation. It is then the most costly phase. Indeed, those costs are due to the difficulty of implementing different changes in the system and to manage their impacts. In this context, much research work has targeted the problem of change impact analysis/prediction. The existent approaches require many inputs that are difficult to extract. In this Master thesis, we propose a probabilistic approach that uses Bayesian classifiers to predict the change impact. These classifiers are trained with historical data about changes. The consider the relations between the elements of a system (input), and the dependencies between the occurred changes (output). More precisely, a complex change in a system is divided into a set of elementary changes. For each type of elementary change, we create a classifier. To predict the impact of complex change, the individual decisions of each classifier are combined using different strategies. We evaluate our approach in two scenarios. In the first, we extract the learning data from the oldest versions of the same system. In the second scenario, the learn data comes from other systems to create the classifiers. This second scenario is interesting because it allows us to use our approach on systems without change histories. Our approach showed that it can predict the impact of elementary changes. The best results are obtained using the classifiers based on conceptual relations. For the prediction of complex changes by the combination of elementary decisions, the results are encouraging considering the few used inputs. More specifically, the voting method and the OR method predict better complex changes when the number of case to analyze is large. Otherwise, using the method Noisy-Or or its modified version is recommended when the number of cases is small.

Les logiciels sont en constante évolution, nécessitant une maintenance et un développement continus. Ils subissent des changements tout au long de leur vie, que ce soit pendant l'ajout de nouvelles fonctionnalités ou la correction de bogues dans le code. Lorsque ces logiciels évoluent, leurs architectures ont tendance à se dégrader avec le temps et deviennent moins adaptables aux nouvelles spécifications des utilisateurs. Elles deviennent plus complexes et plus difficiles à maintenir. Dans certains cas, les développeurs préfèrent refaire la conception de ces architectures à partir du zéro plutôt que de prolonger la durée de leurs vies, ce qui engendre une augmentation importante des coûts de développement et de maintenance. Par conséquent, les développeurs doivent comprendre les facteurs qui conduisent à la dégradation des architectures, pour prendre des mesures proactives qui facilitent les futurs changements et ralentissent leur dégradation. La dégradation des architectures se produit lorsque des développeurs qui ne comprennent pas la conception originale du logiciel apportent des changements au logiciel. D'une part, faire des changements sans comprendre leurs impacts peut conduire à l'introduction de bogues et à la retraite prématurée du logiciel. D'autre part, les développeurs qui manquent de connaissances et–ou d'expérience dans la résolution d'un problème de conception peuvent introduire des défauts de conception. Ces défauts ont pour conséquence de rendre les logiciels plus difficiles à maintenir et évoluer. Par conséquent, les développeurs ont besoin de mécanismes pour comprendre l'impact d'un changement sur le reste du logiciel et d'outils pour détecter les défauts de conception afin de les corriger. Dans le cadre de cette thèse, nous proposons trois principales contributions. La première contribution concerne l'évaluation de la dégradation des architectures logicielles. Cette évaluation consiste à utiliser une technique d’appariement de diagrammes, tels que les diagrammes de classes, pour identifier les changements structurels entre plusieurs versions d'une architecture logicielle. Cette étape nécessite l'identification des renommages de classes. Par conséquent, la première étape de notre approche consiste à identifier les renommages de classes durant l'évolution de l'architecture logicielle. Ensuite, la deuxième étape consiste à faire l'appariement de plusieurs versions d'une architecture pour identifier ses parties stables et celles qui sont en dégradation. Nous proposons des algorithmes de bit-vecteur et de clustering pour analyser la correspondance entre plusieurs versions d'une architecture. La troisième étape consiste à mesurer la dégradation de l'architecture durant l'évolution du logiciel. Nous proposons un ensemble de m´etriques sur les parties stables du logiciel, pour évaluer cette dégradation. La deuxième contribution est liée à l'analyse de l'impact des changements dans un logiciel. Dans ce contexte, nous présentons une nouvelle métaphore inspirée de la séismologie pour identifier l'impact des changements. Notre approche considère un changement à une classe comme un tremblement de terre qui se propage dans le logiciel à travers une longue chaîne de classes intermédiaires. Notre approche combine l'analyse de dépendances structurelles des classes et l'analyse de leur historique (les relations de co-changement) afin de mesurer l'ampleur de la propagation du changement dans le logiciel, i.e., comment un changement se propage à partir de la classe modifiée è d'autres classes du logiciel. La troisième contribution concerne la détection des défauts de conception. Nous proposons une métaphore inspirée du système immunitaire naturel. Comme toute créature vivante, la conception de systèmes est exposée aux maladies, qui sont des défauts de conception. Les approches de détection sont des mécanismes de défense pour les conception des systèmes. Un système immunitaire naturel peut détecter des pathogènes similaires avec une bonne précision. Cette bonne précision a inspiré une famille d'algorithmes de classification, appelés systèmes immunitaires artificiels (AIS), que nous utilisions pour détecter les défauts de conception. Les différentes contributions ont été évaluées sur des logiciels libres orientés objets et les résultats obtenus nous permettent de formuler les conclusions suivantes: • Les métriques Tunnel Triplets Metric (TTM) et Common Triplets Metric (CTM), fournissent aux développeurs de bons indices sur la dégradation de l'architecture. La d´ecroissance de TTM indique que la conception originale de l'architecture s’est dégradée. La stabilité de TTM indique la stabilité de la conception originale, ce qui signifie que le système est adapté aux nouvelles spécifications des utilisateurs. • La séismologie est une métaphore intéressante pour l'analyse de l'impact des changements. En effet, les changements se propagent dans les systèmes comme les tremblements de terre. L'impact d'un changement est plus important autour de la classe qui change et diminue progressivement avec la distance à cette classe. Notre approche aide les développeurs à identifier l'impact d'un changement. • Le système immunitaire est une métaphore intéressante pour la détection des défauts de conception. Les résultats des expériences ont montré que la précision et le rappel de notre approche sont comparables ou supérieurs à ceux des approches existantes. / Software systems evolve, requiring continuous maintenance and development. They undergo changes throughout their lifetimes as new features are added and bugs are fixed. As these systems evolved, their designs tend to decay with time and become less adaptable to changing users'requirements. Consequently, software designs become more complex over time and harder to maintain; in some not-sorare cases, developers prefer redesigning from scratch rather than prolonging the life of existing designs, which causes development and maintenance costs to rise. Therefore, developers must understand the factors that drive the decay of their designs and take proactive steps that facilitate future changes and slow down decay. Design decay occurs when changes are made on a software system by developers who do not understand its original design. On the one hand, making software changes without understanding their effects may lead to the introduction of bugs and the premature retirement of the system. On the other hand, when developers lack knowledge and–or experience in solving a design problem, they may introduce design defects, which are conjectured to have a negative impact on the evolution of systems, which leads to design decay. Thus, developers need mechanisms to understand how a change to a system will impact the rest of the system and tools to detect design defects. In this dissertation, we propose three principal contributions. The first contribution aims to evaluate design decay. Measuring design decay consists of using a diagram matching technique to identify structural changes among versions of a design, such as a class diagram. Finding structural changes occurring in long-lived, evolving designs requires the identification of class renamings. Thus, the first step of our approach concerns the identification of class renamings in evolving designs. Then, the second step requires to match several versions of an evolving design to identify decaying and stable parts of the design. We propose bit-vector and incremental clustering algorithms to match several versions of an evolving design. The third step consists of measuring design decay. We propose a set of metrics to evaluate this design decay. The second contribution is related to change impact analysis. We present a new metaphor inspired from seismology to identify the change impact. In particular, our approach considers changes to a class as an earthquake that propagates through a long chain of intermediary classes. Our approach combines static dependencies between classes and historical co-change relations to measure the scope of change propagation in a system, i.e., how far a change propagation will proceed from a “changed class” to other classes. The third contribution concerns design defect detection. We propose a metaphor inspired from a natural immune system. Like any living creature, designs are subject to diseases, which are design defects. Detection approaches are defense mechanisms if designs. A natural immune system can detect similar pathogens with good precision. This good precision has inspired a family of classification algorithms, artificial Immune Systems (AIS) algorithms, which we use to detect design defects. The three contributions are evaluated on open-source object-oriented systems and the obtained results enable us to draw the following conclusions: • Design decay metrics, Tunnel Triplets Metric (TTM) and Common Triplets Metric (CTM), provide developers useful insights regarding design decay. If TTM decreases, then the original design decays. If TTM is stable, then the original design is stable, which means that the system is more adapted to the new changing requirements. • Seismology provides an interesting metaphor for change impact analysis. Changes propagate in systems, like earthquakes. The change impact is most severe near the changed class and drops off away from the changed class. Using external information, we show that our approach helps developers to locate easily the change impact. • Immune system provides an interesting metaphor for detecting design defects. The results of the experiments showed that the precision and recall of our approach are comparable or superior to that of previous approaches.

During the 21st century, climatic change and non-climatic stressors are likely to impact forests leading to large-scale forest and biodiversity loss, and diminished ecological benefits. Assessing the vulnerability of forests and addressing the sources of vulnerability is an important risk management strategy. The overall goal of this research work is to develop methodological approaches at different scales and apply them to assess the vulnerability of forests in India for developing strategies for forest adaptation. Indicator-based methodological approaches have been developed for vulnerability assessment at local, landscape and national scales under current climate scenario, and at national scale under future climate scenario. Under current climate scenario, the concept of inherent vulnerability of forests has emerged by treating vulnerability as a characteristic internal property of a forest ecosystem independent of exposure. This approach to assess vulnerability is consistent with the framework presented in the latest report of Intergovernmental Panel on Climate Change (IPCC AR5 2014). Assessment of vulnerability under future climate scenario is presented only at national scale due to challenges associated with model-based climate projections and impact assessment at finer scales. The framework to assess inherent vulnerability of forests at local scale involves selection of vulnerability indicators and pair wise comparison method (PCM) to assign the indicator weights. The methodology is applied in the field to a 300-ha moist deciduous case study forest (Aduvalli Protected Forest, Chikmagalur district) in the Western Ghats area, where a vulnerability index value of 0.248 is estimated. Results of the study indicate that two indicators - ‘preponderance of invasive species’ and ‘forest dependence of community’ - are the major drivers of inherent vulnerability at present. The methodology developed to assess the inherent vulnerability at landscape scale involves use of vulnerability indicators, the pair wise comparison method, and geographic information system (GIS) tools. Using the methodology, assessment of inherent vulnerability of Western Ghats Karnataka (WGK) landscape forests is carried out. Four vulnerability indicators namely, biological richness, disturbance index, canopy cover and slope having weights 0.552, 0.266, 0.123 and 0.059, respectively are used. The study shows that forests at one-third of the grid points in the landscape have high and very high inherent vulnerability, and natural forests are inherently less vulnerable than plantation forests. The methodology used for assessment of forest inherent vulnerability at the national scale was same as used at landscape scale. 40% of forest grid points in India are assessed with high and very high inherent vulnerability. Except in pockets, the forests in the three biodiversity hotspots in India i.e., the Western Ghats in peninsular India, northeastern India, and the northern Himalayan region are assessed to have low to medium inherent vulnerability. Vulnerability of forests under future climate scenario at national scale is estimated by combining the results of assessment of climate change impact and inherent vulnerability. In the present study, ensemble climatology from five CMIP5 (Coupled Model Intercomparison Project phase 5) climate models for RCP (Representative Concentration Pathways) 4.5 and 8.5 in short (2030s) and long term (2080s) is used as input to IBIS (Integrated Biosphere Simulator) dynamic vegetation model. Forest grid points projected to experience vegetation-shift to a new plant functional type (PFT) under future climate are categorized under ‘extremely high’ vulnerability category. Such forest grid points in India are 22 and 23% in the short term under RCP4.5 and 8.5 respectively, and these percentages increase to 31 and 37% in the long term. IBIS simulated vegetation projections are also compared with LPJ (Lund-Potsdam-Jena) simulated projections. Both the vegetation models agree that forests at about one-third of the grid points could be impacted by future climate but the spatial distribution of impacted grid points differs between the models. Vulnerability assessment is a powerful tool for building long-term resilience in the forest sector in the context of projected climate change. From this study, three forest scenarios emerge in India for developing adaptation strategies namely: (a) less disturbed primary forests; (b) degraded and fragmented primary forests; and (c) secondary (plantation) forests. Minimizing anthropogenic disturbance and conserving biodiversity are critical to reduce forest vulnerability of less disturbed primary forests. For disturbed forests and plantations, adaptive management aimed at forest restoration is necessary to build resilience. Mainstreaming forest adaptation in India through Forest Working Plans and realignment of the forestry programs is necessary to manage the risk to forests under climate change.

abstract: Energy consumption in buildings, accounting for 41% of 2010 primary energy consumption in the United States (US), is particularly vulnerable to climate change due to the direct relationship between space heating/cooling and temperature. Past studies have assessed the impact of climate change on long-term mean and/or peak energy demands. However, these studies usually neglected spatial variations in the “balance point” temperature, population distribution effects, air-conditioner (AC) saturation, and the extremes at smaller spatiotemporal scales, making the implications of local-scale vulnerability incomplete. Here I develop empirical relationships between building energy consumption and temperature to explore the impact of climate change on long-term mean and extremes of energy demand, and test the sensitivity of these impacts to various factors. I find increases in summertime electricity demand exceeding 50% and decreases in wintertime non-electric energy demand of more than 40% in some states by the end of the century. The occurrence of the most extreme (appearing once-per-56-years) electricity demand increases more than 2600 fold, while the occurrence of the once per year extreme events increases more than 70 fold by the end of this century. If the changes in population and AC saturation are also accounted for, the impact of climate change on building energy demand will be exacerbated. Using the individual building energy simulation approach, I also estimate the impact of climate change to different building types at over 900 US locations. Large increases in building energy consumption are found in the summer, especially during the daytime (e.g., >100% increase for warehouses, 5-6 pm). Large variation of impact is also found within climate zones, suggesting a potential bias when estimating climate-zone scale changes with a small number of representative locations. As a result of climate change, the building energy expenditures increase in some states (as much as $3 billion/year) while in others, costs decline (as much as $1.4 billion/year). Integrated across the contiguous US, these variations result in a net savings of roughly $4.7 billion/year. However, this must be weighed against the cost (exceeding $19 billion) of adding electricity generation capacity in order to maintain the electricity grid’s reliability in summer. / Dissertation/Thesis / Doctoral Dissertation Environmental Social Science 2016

No description available.

Research leading to this thesis aims to assess the policy relevant mitigation potential of Indian forests as well as aims to assess the impact of climate change on carbon stocks, vegetation boundary shifts, Net Primary Productivity (NPP) and the mitigation potential of Indian forests. To project the impact of climate change we chose a dynamic global vegetation model ‘Integrated Biosphere Simulator’ (IBIS V.2.6b4). We selected A2 and B2 scenarios for projecting the impacts. Mitigation potential was assessed using the ‘Generalized Comprehensive Mitigation Assessment Process’ (GCOMAP) model. We assess the mitigation potential of Indian forests in the light of India’s long-term policy objective of bringing 33% of its total geographical area under forest cover. We analyzed the mitigation potential of this policy objective under two scenarios: the first comprising of rapid afforestation scenario with the target to achieving the goal by 2020 and the second a moderate afforestation scenario in which this goal is achieved by 2030. We estimate that afforestation could offset about 9% of India’s average national emissions over the 2010-2030 period, while about 6.7% could be mitigated under the moderate afforestation scenario over the same period. We analyze the impact of climate change on the four key attributes of Indian forests, i.e. impact on vegetation distribution, impact on forest productivity (NPP), impact on soil carbon (SOC) and impact on biomass carbon. IBIS simulations suggest that approximately 39% and 34% of forest grids are projected to experience change in vegetation type under A2 and B2 climate scenarios, respectively over the period 2070¬2100. Simulations further indicate that NPP is projected to increase by an average of 66% under the A2 scenario and 49% under the B2 scenario. The increase is higher in the northeastern part of India. However, in the central and western Indian forests NPP remains stable or increases only moderately, and in some places even decreases. Our assessment of the impact of climate change on Soil Organic Carbon (SOC) suggests a trend similar to NPP distribution, which is to be expected as increased NPP is the primary driver of higher litter input to the soil. However, the quantum of increase in this case is lower, around 37% and 30%, for the A2 and B2 scenario respectively (averaged over India). The biomass carbon is also projected to increase all over India on the lines similar to NPP gains. However, projected gains in biomass, NPP and SOC should be viewed with caution as IBIS tends to simulate a fairly strong CO2 fertilization effect that may not necessarily be realized under conditions of nutrient and water limitations and under conditions of increased pest and fire outbreaks. Further we analyzed the impact of climate change on the mitigation potential of Indian forests by linking impact assessment models to mitigation potential assessment model GCOMAP. Two impact assessment models BIOME4 and IBIS are used for simulating the impact of climate change. IBIS is a dynamic vegetation model while BIOME4 is an equilibrium model. Our assessment suggests that with the BIOME4 simulations the cumulative mitigation potential increases by up to 21% under the A2 scenario over the period 2008 to 2108, whereas, under the B2 scenario the mitigation potential increases only by 14% over the same period. However cumulative mitigation potential estimates obtained from the IBIS simulations suggest much smaller gains, where mitigation potential increases by only 6% and 5% over the period 2008 to 2108, under A2 and B2 scenarios respectively. To enable effective policy analysis and to build a synergy between the mitigation and adaptation efforts in the Indian forest sector, a vulnerability index for the forested grids is constructed. The vulnerability index is based on the premise that forests in India are already subjected to multiple stresses including over extraction, insect outbreaks, live¬stock grazing, forest fires and other anthropogenic pressures -with climate change being an additional stress. The forest vulnerability index suggests that nearly 39% of the forest grids in India are projected to be vulnerable to the impacts of climate change under the A2 scenario, while 34% of the forest grids are projected to be vulnerable under the B2 scenario. The vulnerability index suggests that forests in the central part of India, a significant part of the western Himalayan forests and northern and central parts of the Western Ghats are particularly vulnerable to the impacts of climate change. Forests in the northeastern part of India are seemingly resilient to the impacts of climate change. It also suggests that given the high deforestation rate in northeast, this region be prioritized for reducing deforestation and forest degradation (REDD) projects under the United Nations Framework Convention on Climate Change (UNFCCC) mechanisms.