| dc.description.abstract | India’s per capita CO2 emissions increased from 0.8 to 1.7 metric tonnes from 1990 to 2010; however it was well below the world average of 4.9 metric tonnes in 2010. The major contributor to the emissions are the electricity sector and the industrial sector. Though as present the share of transport sector in India’s emissions is only 7%, emissions from this sector are growing at a fast pace . A large part of the transport sector emissions come from Road (87%) whereas Railways contribute by about 5% of the emissions. Oil is the major fuel consumed for transport in India currently. Rise in population and incomes would lead to a rise in demand for public as well as commercial modes of transportation. There are many India specific studies that focus on the aggregate transportation sector as well as passenger transportation sector from the emissions perspective. There is a clear research gap in terms of a more insightful understanding of the emission profile and mitigation opportunities in India’s freight sector, as there are comparatively few studies that focus on this sector. Greater economic and industrial growth would increase freight transport demand in India, leading to increasing impact on energy and emissions. It is also well documented that India’s freight transportation is increasingly relying on inefficient trucks for long distance transportation, which also has important implications for India’s emission mitigation strategy. For a deeper understanding of energy and emissions profile of this sector and ways to address climate policy concerns, the study addresses the following research questions : i) How will the share of freight modes evolve in the business as usual (BAU) and ADVANCED Scenarios? ii) What will be the energy and emission development of freight transport demand under BAU scenario? iii) What will be the energy and emission development of freight transport demand under Carbon tax scenarios? iv) How will the freight structure change under a carbon constraint specifically the Energy demand and service output, Global carbon price in different time periods? The first and second research questions focus on improving our understanding of the long term evolution of the freight sector, while the last two questions focus on the impact of climate policies on India’s freight sector. GCAM, an Integrated Assessment Model has been selected to conduct the study because of the following reasons: 1) Transport sector focus, 2) Long term framework, 3) Study of evolution, 4) Global database but with regional focus, 5) options of studying carbon constraint and carbon tax. Scenario analysis has been performed to understand the evolution of freight structure parameters in the long run. Along with the BAU scenario, there is a carbon constraint scenario restricting the emissions to 450 parts per million (ppm). There are four carbon tax scenarios starting with imposition of a tax on carbon emission from the year 2020 and increasing it by 5% every year. The set of scenarios focused on climate policies is to understand the response of freight sector to varying stringency of climate policies. In the BAU case, energy demand of the passenger sector is the highest in India (within transportation sector) and increases steadily over the years for all transport modes. Among the competing modes of the freight sector, major share of energy demand in India is by road comprising about 80% of the total share. The share of air, domestic ship and rail stays around 5-7% each. The cost of freight transport modes is highest for air followed by the road sector both in India and the world. Still in India, in the last decade and a half we have seen a consistent decline in the share of freight transportation by rail, and freight movement has shifted towards road. China is the leading emitter in the world of CO2 over the period of time surpassing U.S.A in 2005, driven by its economic growth. The emissions of India and South East Asia increase significantly over the period. At the time of 2100, the BAU scenario leads to an increase in the Global mean temperature by 3.74oC, much higher than the accepted limit of 2oC. Within any climate policy scenario, as well as across scenarios, the energy demand decreases as the carbon tax increases. This is because of an increase in price of fossil fuel resulting in the increase in the price of the sub-sector as most of the transport modes use them for their energy needs. The energy demand of the carbon constraint scenario is lower than carbon tax scenarios due to higher emission constraints applied at earlier years, which implies a higher implicit carbon tax. For the freight sector modes, the energy consumption of air, rail and road increases continuously over the long run but that of domestic ship starts decreasing for the carbon tax scenarios in the second half of the century. This is because of higher technology substitution possibilities within the rail and road sectors, and low price elasticity of air freight transport due to lack of alternatives to high speed air travel. With a carbon tax, share of high cost road decreases while the share of low cost rail increases through the years. Also rail freight can be carried by use of electricity which is highly efficient as compared to technologies which use fossil fuel. Service output of rail increases at an increasing rate. Service output of road also increases with the BAU scenario having the highest service output. An interesting finding is that the increase in service output of the rail mode is directly proportional to the carbon tax applied. Cost of rail decreases after year 2040 due to technology improvements in electric trains and corresponding decrease in costs. The costs of road sector increases over the long run. For the carbon constraint scenario, the carbon price in 2020 is 134 1990$/tc and increases to 304 1990$/tc in 2095. Transport sector emissions increase for all the scenarios. Along with the carbon constraint scenario , two of the carbon tax scenarios (with initial tax at 20 and 25 (1990 $/tc) also limit the temperatures to less than 2.50C. There are some key overall insights from the analysis. First, in absence of any dedicated climate policy intervention, energy consumption and resultant carbon dioxide emissions from India’s freight sector will increase significantly in the long run, with a high reliance on road transportation modes. Second, a climate policy in the form of a carbon tax will be successful in reducing direct emissions from this sector and the extent of decline will depend on the stringency of climate policy. Reduction in emissions due to climate policy will happen due to the following responses: a) Reduction in overall freight transportation with higher prices, b) a modal shift towards rail from trucks, and c) a shift towards electricity based transport especially electric rail for moving freight. These insights clearly highlight the importance of two ongoing interventions by the Government of India. Dedicated freight corridors that are being planned and implemented to connect different regions in India through dedicated freight transportation infrastructure will have a positive impact in terms of emission reduction from this sector. More importantly, the electrification of railway is a critical step. If freight transportation train lines are electrified, then there will be significant dent in direct emissions from this sector. The dedicated freight corridor should also rely mainly on electricity as a fuel. In addition to these, increasing the fuel efficiency of freight trucks is also an important step in the direction of reducing energy use and emissions from India’s freight transportation sector. The research contributes by bringing out the energy and emission evolution of the transport sector in India with a focus on freight sector. The share and characteristics of different modes of freight transport including energy input, service output, cost, carbon price and emissions over the long run under different scenarios, it is hoped, will give the policymakers an understanding of transport sector evolution and aid in their decision making. | en_US |
