Trade wars in the TRIPS Council: Intellectual property, technology transfer, and climate change

This Chapter considers the geopolitical conflicts in respect of intellectual property, trade, and climate change in the TRIPS Agreement 1994 under the World Trade Organization (WTO). In particular, it focuses upon debates in the TRIPS Council on the topic of patent law and clean energy in 2013 and 2014. The chapter highlights the development agenda of a number of developing countries who are keen for access to clean energy to combat climate change and global warming. It also considers the mixed contributions of members of the BRICS/ BASIC group – including Brazil, India, China, and South Africa. This chapter highlights the intellectual property maximalist position of a number of developed countries on intellectual property, climate change, and trade. Seeking to overcome this conflict and stalemate, this Chapter puts forward both procedural and substantial reform options in respect of intellectual property, trade, and climate change in the TRIPS Council and the WTO. It also flags that the TRIPS Agreement 1994 could well be displaced by the rise of mega-regional trade agreements – such as the Trans-Pacific Partnership (TPP), and the Trans-Atlantic Trade and Investment Partnership (TTIP).

Importance of carbon dioxide physiological forcing to future climate change

An increase in atmospheric carbon dioxide (CO2) concentration influences climate both directly through its radiative effect (i.e., trapping longwave radiation) and indirectly through its physiological effect (i.e., reducing transpiration of land plants). Here we compare the climate response to radiative and physiological effects of increased CO2 using the National Center for Atmospheric Research (NCAR) coupled Community Land and Community Atmosphere Model. In response to a doubling of CO2, the radiative effect of CO2 causes mean surface air temperature over land to increase by 2.86 ± 0.02 K (± 1 standard error), whereas the physiological effects of CO2 on land plants alone causes air temperature over land to increase by 0.42 ± 0.02 K. Combined, these two effects cause a land surface warming of 3.33 ± 0.03 K. The radiative effect of doubling CO2 increases global runoff by 5.2 ± 0.6%, primarily by increasing precipitation over the continents. The physiological effect increases runoff by 8.4 ± 0.6%, primarily by diminishing evapotranspiration from the continents. Combined, these two effects cause a 14.9 ± 0.7% increase in runoff. Relative humidity remains roughly constant in response to CO2-radiative forcing, whereas relative humidity over land decreases in response to CO2-physiological forcing as a result of reduced plant transpiration. Our study points to an emerging consensus that the physiological effects of increasing atmospheric CO2 on land plants will increase global warming beyond that caused by the radiative effects of CO2.

Impact of climate change on Indian forests: a dynamic vegetation modeling approach

We make an assessment of the impact of projected climate change on forest ecosystems in India. This assessment is based on climate projections of the Regional Climate Model of the Hadley Centre (HadRM3) and the dynamic global vegetation model IBIS for A2 and B2 scenarios. According to the model projections, 39% of forest grids are likely to undergo vegetation type change under the A2 scenario and 34% under the B2 scenario by the end of this century. However, in many forest dominant states such as Chattisgarh, Karnataka and Andhra Pradesh up to 73%, 67% and 62% of forested grids are projected to undergo change. Net Primary Productivity (NPP) is projected to increase by 68.8% and 51.2% under the A2 and B2 scenarios, respectively, and soil organic carbon (SOC) by 37.5% for A2 and 30.2% for B2 scenario. Based on the dynamic global vegetation modeling, we present a forest vulnerability index for India which is based on the observed datasets of forest density, forest biodiversity as well as model predicted vegetation type shift estimates for forested grids. The vulnerability index suggests that upper Himalayas, northern and central parts of Western Ghats and parts of central India are most vulnerable to projected impacts of climate change, while Northeastern forests are more resilient. Thus our study points to the need for developing and implementing adaptation strategies to reduce vulnerability of forests to projected climate change.

Climate change and forests in India: adaptation opportunities and challenges

Climate change is projected to lead to shift of forest types leading to irreversible damage to forests by rendering several species extinct and potentially affecting the livelihoods of local communities and the economy. Approximately 47% and 42% of tropical dry deciduous grids are projected to undergo shifts under A2 and B2 SRES scenarios respectively, as opposed to less than 16% grids comprising of tropical wet evergreen forests. Similarly, the tropical thorny scrub forest is projected to undergo shifts in majority of forested grids under A2 (more than 80%) as well as B2 scenarios (50% of grids). Thus the forest managers and policymakers need to adapt to the ecological as well as the socio-economic impacts of climate change. This requires formulation of effective forest management policies and practices, incorporating climate concerns into long-term forest policy and management plans. India has formulated a large number of innovative and progressive forest policies but a mechanism to ensure effective implementation of these policies is needed. Additional policies and practices may be needed to address the impacts of climate change. This paper discusses an approach and steps involved in the development of an adaptation framework as well as policies, strategies and practices needed for mainstreaming adaptation to cope with projected climate change. Further, the existing barriers which may affect proactive adaptation planning given the scale, accuracy and uncertainty associated with assessing climate change impacts are presented.

Forest policies and programs affecting vulnerability and adaptation to climate change

Due to large scale afforestation programs and forest conservation legislations, India's total forest area seems to have stabilized or even increased. In spite of such efforts, forest fragmentation and degradation continues, with forests being subject to increased pressure due to anthropogenic factors. Such fragmentation and degradation is leading to the forest cover to change from very dense to moderately dense and open forest and 253 km(2) of very dense forest has been converted to moderately dense forest, open forest, scrub and non-forest (during 2005-2007). Similarly, there has been a degradation of 4,120 km(2) of moderately dense forest to open forest, scrub and non-forest resulting in a net loss of 936 km(2) of moderately dense forest. Additionally, 4,335 km(2) of open forest have degraded to scrub and non-forest. Coupled with pressure due to anthropogenic factors, climate change is likely to be an added stress on forests. Forest sector programs and policies are major factors that determine the status of forests and potentially resilience to projected impacts of climate change. An attempt is made to review the forest policies and programs and their implications for the status of forests and for vulnerability of forests to projected climate change. The study concludes that forest conservation and development policies and programs need to be oriented to incorporate climate change impacts, vulnerability and adaptation.

Impact of climate change at species level: a case study of teak in India

In this study, we model the long-term effect of climate change on commercially important teak (Tectona grandis) and its productivity in India. This modelling assessment is based on climate projections of the regional climate model of the Hadley Center (HadRM3) and the dynamic vegetation model, IBIS. According to the model projections, 30% of teak grids in India are vulnerable to climate change under both A2 and B2 SRES scenarios because the future climate may not be optimal for teak at these grids. However, the net primary productivity and biomass are expected to increase because of elevated levels of CO2. Given these directions of likely impacts, it is crucial to further investigate the climate change impacts on teak and incorporate such findings into long-term teak plantation programs. This study also demonstrates the feasibility and limitations of assessing the impact of projected climate change at the species level in the tropics.

Implications of climate change on mitigation potential estimates for forest sector in India

Climate change is projected to impact forest ecosystems, including biodiversity and Net Primary Productivity (NPP). National level carbon forest sector mitigation potential estimates are available for India; however impacts of projected climate change are not included in the mitigation potential estimates. Change in NPP (in gC/m(2)/yr) is taken to represent the impacts of climate change. Long term impacts of climate change (2085) on the NPP of Indian forests are available; however no such regional estimates are available for short and medium terms. The present study based on GCM climatology scenarios projects the short, medium and long term impacts of climate change on forest ecosystems especially on NPP using BIOME4 vegetation model. We estimate that under A2 scenario by the year 2030 the NPP changes by (-5) to 40% across different agro-ecological zones (AEZ). By 2050 it increases by 15% to 59% and by 2070 it increases by 34 to 84%. However, under B2 scenario it increases only by 3 to 25%, 3.5 to 34% and (-2.5) to 38% respectively, in the same time periods. The cumulative mitigation potential is estimated to increase by up to 21% (by nearly 1 GtC) under A2 scenario between the years 2008 and 2108, whereas, under B2 the mitigation potential increases only by 14% (646 MtC). However, cumulative mitigation potential estimates obtained from IBIS-a dynamic global vegetation model suggest much smaller gains, where mitigation potential increases by only 6% and 5% during the period 2008 to 2108.

A macroeconomic analysis of adaptation to climate change impacts on forests in India

We examine the potential for adaptation to climate change in Indian forests, and derive the macroeconomic implications of forest impacts and adaptation in India. The study is conducted by integrating results from the dynamic global vegetation model IBIS and the computable general equilibrium model GRACE-IN, which estimates macroeconomic implications for six zones of India. By comparing a reference scenario without climate change with a climate impact scenario based on the IPCC A2-scenario, we find major variations in the pattern of change across zones. Biomass stock increases in all zones but the Central zone. The increase in biomass growth is smaller, and declines in one more zone, South zone, despite higher stock. In the four zones with increases in biomass growth, harvest increases by only approximately 1/3 of the change in biomass growth. This is due to two market effects of increased biomass growth. One is that an increase in biomass growth encourages more harvest given other things being equal. The other is that more harvest leads to higher supply of timber, which lowers market prices. As a result, also the rent on forested land decreases. The lower prices and rent discourage more harvest even though they may induce higher demand, which increases the pressure on harvest. In a less perfect world than the model describes these two effects may contribute to an increase in the risk of deforestation because of higher biomass growth. Furthermore, higher harvest demands more labor and capital input in the forestry sector. Given total supply of labor and capital, this increases the cost of production in all the other sectors, although very little indeed. Forestry dependent communities with declining biomass growth may, however, experience local unemployment as a result.

Integrated modelling approaches to analysis of climate change impacts on forests and forest management

This paper reviews integrated economic and ecological models that address impacts and adaptation to climate change in the forest sector. Early economic model studies considered forests as one out of many possible impacts of climate change, while ecological model studies tended to limit the economic impacts to fixed price-assumptions. More recent studies include broader representations of both systems, but there are still few studies which can be regarded fully integrated. Full integration of ecological and economic models is needed to address forest management under climate change appropriately. The conclusion so far is that there are vast uncertainties about how climate change affects forests. This is partly due to the limited knowledge about the global implications of the social and economical adaptation to the effects of climate change on forests.

Climate change and agricultrue-An Indian perspective

In this paper, we examine the major predictions made so far regarding the nature of climate change and its impacts on our region in the light of the known errors of the set of models and the observations over this century. The major predictions of the climate models about the impact of increased concentration of greenhouse gases ave at variance with the observations over the Indian region during the last century characterized by such increases and global warming. It is important to note that as far as the Indian region is concerned, the impact of year-to-year variation of the monsoon will continue to be dominant over longer period changes even in the presence of global warming. Recent studies have also brought out the uncertainties in the yields simulated by crop models. It is suggested that a deeper understanding of the links between climate and agricultural productivity is essential for generating reliable predictions of impact of climate change. Such an insight is also required for identifying cropping patterns and management practices which are tailored for sustained maximum yield in the face of the vagaries of the monsoon.

Climate change and Indian forests

An assessment of the impact of projected climate change on forest ecosystems in India based on climate projections of the Regional Climate Model of the Hadley Centre (HadRM3) and the global dynamic vegetation model IBIS for A1B scenario is conducted for short-term (2021-2050) and long-term (2071-2100) periods. Based on the dynamic global vegetation modelling, vulnerable forested regions of India have been identified to assist in planning adaptation interventions. The assessment of climate impacts showed that at the national level, about 45% of the forested grids is projected to undergo change. Vulnerability assessment showed that such vulnerable forested grids are spread across India. However, their concentration is higher in the upper Himalayan stretches, parts of Central India, northern Western Ghats and the Eastern Ghats. In contrast, the northeastern forests, southern Western Ghats and the forested regions of eastern India are estimated to be the least vulnerable. Low tree density, low biodiversity status as well as higher levels of fragmentation, in addition to climate change, contribute to the vulnerability of these forests. The mountainous forests (sub-alpine and alpine forest, the Himalayan dry temperate forest and the Himalayan moist temperate forest) are susceptible to the adverse effects of climate change. This is because climate change is predicted to be larger for regions that have greater elevations.

Support Vector Machine Approach to Downscale Precipitation in Climate Change Scenarios

Concern over changes in global climate has increased in recent years with improvement in understanding of atmospheric dynamics and growth in evidence of climate link to long‐term variability in hydrologic records. Climate impact studies rely on climate change information at fine spatial resolution. Towards this, the past decade has witnessed significant progress in development of downscaling models to cascade the climate information provided by General Circulation Models (GCMs) at coarse spatial resolution to the scale relevant for hydrologic studies. While a plethora of downscaling models have been applied successfully to mid‐latitude regions, a few studies are available on tropical regions where the atmosphere is known to have more complex behavior. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling to interpret climate change signals provided by GCMs over tropical regions of India. Climate variables affecting spatio‐temporal variation of precipitation at each meteorological sub‐division of India are identified. Following this, cluster analysis is applied on climate data to identify the wet and dry seasons in each year. The data pertaining to climate variables and precipitation of each meteorological sub‐division is then used to develop SVM based downscaling model for each season. Subsequently, the SVM based downscaling model is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to assess the impact of climate change on hydrological inputs to the meteorological sub‐divisions. The results obtained from the SVM downscaling model are then analyzed to assess the impact of climate change on precipitation over India.

Modeling the vulnerability of an urban groundwater system due to the combined impacts of climate change and management scenarios

Climate change impact on a groundwater-dependent small urban town has been investigated in the semiarid hard rock aquifer in southern India. A distributed groundwater model was used to simulate the groundwater levels in the study region for the projected future rainfall (2012-32) obtained from a general circulation model (GCM) to estimate the impacts of climate change and management practices on groundwater system. Management practices were based on the human-induced changes on the urban infrastructure such as reduced recharge from the lakes, reduced recharge from water and wastewater utility due to an operational and functioning underground drainage system, and additional water extracted by the water utility for domestic purposes. An assessment of impacts on the groundwater levels was carried out by calibrating a groundwater model using comprehensive data gathered during the period 2008-11 and then simulating the future groundwater level changes using rainfall from six GCMs Institute of Numerical Mathematics Coupled Model, version 3.0 (INM-CM. 3.0); L'Institut Pierre-Simon Laplace Coupled Model, version 4 (IPSL-CM4); Model for Interdisciplinary Research on Climate, version 3.2 (MIROC3.2); ECHAM and the global Hamburg Ocean Primitive Equation (ECHO-G); Hadley Centre Coupled Model, version 3 (HadCM3); and Hadley Centre Global Environment Model, version 1 (HadGEM1)] that were found to show good correlation to the historical rainfall in the study area. The model results for the present condition indicate that the annual average discharge (sum of pumping and natural groundwater outflow) was marginally or moderately higher at various locations than the recharge and further the recharge is aided from the recharge from the lakes. Model simulations showed that groundwater levels were vulnerable to the GCM rainfall and a scenario of moderate reduction in recharge from lakes. Hence, it is important to sustain the induced recharge from lakes by ensuring that sufficient runoff water flows to these lakes.

Climate change scenarios of surface solar radiation in data sparse regions: a case study in Malaprabha River Basin, India

A variety of methods are available to estimate future solar radiation (SR) scenarios at spatial scales that are appropriate for local climate change impact assessment. However, there are no clear guidelines available in the literature to decide which methodologies are most suitable for different applications. Three methodologies to guide the estimation of SR are discussed in this study, namely: Case 1: SR is measured, Case 2: SR is measured but sparse and Case 3: SR is not measured. In Case 1, future SR scenarios are derived using several downscaling methodologies that transfer the simulated large-scale information of global climate models to a local scale ( measurements). In Case 2, the SR was first estimated at the local scale for a longer time period using sparse measured records, and then future scenarios were derived using several downscaling methodologies. In Case 3: the SR was first estimated at a regional scale for a longer time period using complete or sparse measured records of SR from which SR at the local scale was estimated. Finally, the future scenarios were derived using several downscaling methodologies. The lack of observed SR data, especially in developing countries, has hindered various climate change impact studies. Hence, this was further elaborated by applying the Case 3 methodology to a semi-arid Malaprabha reservoir catchment in southern India. A support vector machine was used in downscaling SR. Future monthly scenarios of SR were estimated from simulations of third-generation Canadian General Circulation Model (CGCM3) for various SRES emission scenarios (A1B, A2, B1, and COMMIT). Results indicated a projected decrease of 0.4 to 12.2 W m(-2) yr(-1) in SR during the period 2001-2100 across the 4 scenarios. SR was calculated using the modified Hargreaves method. The decreasing trends for the future were in agreement with the simulations of SR from the CGCM3 model directly obtained for the 4 scenarios.