Energy Flow Analysis of China 2050 Pathways Energy Calculator with Special Emphasis on Transportation 1876 6102 © 2016 The Authors Published by Elsevier Ltd This is an open access article under the CC[.]
Available online at www.sciencedirect.com ScienceDirect Energy Procedia 104 (2016) 275 – 280 CUE2016-Applied Energy Symposium and Forum 2016: Low carbon cities & urban energy systems Energy flow analysis of China 2050 Pathways Energy Calculator with special emphasis on transportation Steve-Wonder Amakpaha, Gengyuan Liua,b *, Yan Haoa,b,*, Linyu Xua,b a State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China b Beijing Engineering Research Center for Watershed Environmental Restoration & Integrated Ecological Regulation, Beijing 100875, China Abstract Energy optimisation and CO2 emission reduction cannot be achieved without a holistic analysis of transportation This research performs a critical analysis of the China 2050 Pathways Calculator with emphasis on its ability to assess long-term energy optimisation and sustainability of the energy-dependent transport sector It focuses on the structure, methods, assumptions and the energy mix, with emphasis on its application to the transportation system in China Employing a hybrid energy flow- and meta-analysis, the paper holistically aims to find the relative quantitative importance of mitigation options in each set indicators available for energy optimisation in transport pathways It compares the combined energy flows (input/output) data and assumptions by applying the formula Pw=kTI to identify the number of pathways (including energy-mix) available in the transport stream of the Calculator – holding all other sectors and their trajectories at default where k=1 Initial results indicate that there are 262,144 ways for selecting transport pathways alone The framework of analysis and implicit normative assumption determine the focus of mitigating options, in terms of both descriptive emphasis and qualitative evaluation The usefulness of predicting future energy-mix transformation for all energy-dependent sectors of an economy cannot be overemphasised However, the huge data set needed and time required to gather such data, if available, to build the model creates too much room for assumptions that could mar the credibility of the outcomes This research considers the cost involved to build such an integrated model to be a “turn-off” for most policy makers to start such an endeavour Stemming from the lapses identified in the China 2050 Pathways Calculator, this paper suggests that considerable refinement should be made to the various assumptions in the trajectories to reflect achievable energy/climate change targets by 2050 Also, policymakers should develop an affinity for patience and be committed financially in the search for improved scientific solutions toward energy optimisation and GHG emission reduction © 2016 The Authors Published by Elsevier Ltd This is an open access article under the CC BY-NC-ND license © 2016 The Authors Published by Elsevier Ltd (http://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and/or peer-review of under responsibility of CUE Peer-review under responsibility the scientific committee of the Applied Energy Symposium and Forum, CUE2016: Low carbon cities and urban energy systems Keywords: 2050 Pathways Calculator; energy flows; energy optimisation; transportation; GHG emission * Corresponding author Tel.: +86-13811146331 E-mail address: liugengyuan@bnu.edu.cn (G.Y LIU), haoyan@bnu.edu.cn (Y HAO) 1876-6102 © 2016 The Authors Published by Elsevier Ltd This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the Applied Energy Symposium and Forum, CUE2016: Low carbon cities and urban energy systems doi:10.1016/j.egypro.2016.12.047 276 Steve-Wonder Amakpah et al / Energy Procedia 104 (2016) 275 – 280 Introduction In the face of rising global concerns on GHG emissions, energy demand for fossil fuel energydependent sectors – which serve as an engine of growth to most striving economies – is still on the ascendant As of 2012, China’s Total Primary Energy Consumption (TPEC) and its corresponding CO2 Emission from Consumption of Energy stands at about 105.9 Quadrillion Btu and 8106Mtce respectively, taking the first spot ahead of the USA.[1] While consuming a little above 20% of the world’s TPEC, China’s CO2 emission is a quarter (25%) of the world’s total These alarming figures not only threaten the environment, ecology, and socio-economic landscapes of China, but have become a global concern The rising concerns about the potential global energy crisis and its impact on the economy and environment calls for a transition from the current development paradigm to a sustainable one.[2] The continual reliance on fossil energy and its consequential environmental pollution and ecosystem degradation need global energy governance Why we need global energy governance? In an attempt to answer this question, Neil Hirst said, “everyone…is well aware of the gravity of the energy-related problems that we face Those of energy supply and security are probably the most pressing The problem of climate change is the most profound We need a transformation in the technology and culture of energy use and supply.”† In an attempt to mitigate global energy crises, in recent years many groundbreaking technologies have been invented to develop, improve and manage more renewable, eco-friendly energies and reduce overall carbon emission One example is Ubiquitous Energy or Internet-of-Things To supplement these high-tech novelties, scientists and engineers have created some innovative policy-driven models to help influence the culture of energy use and supply Most of these models are computer-based simulations that are user-friendly, such as the Ecological Footprint Calculator, Water Footprint Calculator and now the 2050 Pathways Calculator For mitigating climate change, transportation increasingly moves into the spotlight [3] More than a quarter of overall energy use is allocated to the transportation sector, causing 22% of global energy enduse-related CO2 emission [4] Three-quarters of these emissions originate in road vehicles, and half of the latter in urban transport [5] To buttress this claim, Kahn Ribeiro et al.,(2012) [6] and Schäfer et al.,(2009) [7] opined that the CO2 emissions from transport increase faster than those in other sectors, as developing economies rely increasingly on the transport sector with structural change from the industrial to the service sector In China, about 16% of CO2 emission comes from petroleum (oil reserves and imports) products according to EIA [1], whereas energy input from oil is 22%, out of which transportation alone uses about 90% of the available energy after generation and transmission loss The remaining 10% finds its way into the industry In addition to that, the Paris Agreement in the framework of the United Nations Framework Convention on Climate Change (UNFCCC) – mandates nations to take holistic actions toward energy security and GHG emissions reduction Even though the agreement is due to be activated in 2020, many countries, including China, have adopted their own 2050 Pathways Energy Calculator Originally developed by the UK’s Department of Energy and Climate Change (DECC), more than 20 countries have now developed or are developing their own calculator Professor David MacKay, chief scientific advisor to DECC created this unique open-source analysis package called the 2050 Pathway Calculator [8] This paper investigates the structure of China 2050 Pathways Calculator, with focus on energy flows, measurable indicators, assumptions and energy-mix for the transportation sector Employing a hybrid energy flow- and meta-analysis, the paper holistically aims to find the relative quantitative importance of mitigation options in each set of indicators available for energy optimisation in the transport pathways † Presentation by NEIL HIRST (Senior Fellow of the Grantham Institute for Climate Change, Imperial College London) at GLOBAL ENERGY GOVERNANCE REFORM to the BEIJING ENERGY CLUB on 19 September 2012 Steve-Wonder Amakpah et al / Energy Procedia 104 (2016) 275 – 280 We analyse nine indicators: Inner City Transport Demand (TDin), Inner City Travelling Method (TMin), Usage of Green Vehicles (UGV), Inter-city Transport Demand (TDit), Inter-city Travelling Method (TMit), International Travel Demand (ITD), Freight Transport Demand and Method (FTDM), Freight Vehicles (FV), International Freight Demand (IFD) designated as 1, 2, 3…, respectively (Table 1) For each indicator, there are four trajectories (scenarios), designated as A, B, C, and D within the transportation pathway Results indicate that transport cannot be overlooked or taken lightly when evaluating energy supply-demand and CO2 emissions The aim of this research is to fill the literature gap in the energy-transportation nexus and the 2050 Pathways Calculator, especially relating to China and create awareness of critical energy governance issues We conclude that the framework of analysis and implicit normative assumption determine the focus of mitigation options, in terms of both descriptive emphasis and quantitative evaluation 2050 Pathways Energy Calculator – How China joined the “wagon” The 2050 Pathways Energy Calculator, also known as “2050 Pathways” is a generic simulation model built by the UK’s Department of Energy and Climate Change (DECC) with the aim of providing a tool for policy making on energy The UK passed pioneering legislation with the Climate Change Act in 2008, which set ambitious carbon emissions reduction targets of 36% by 2020 and 8% by 2050, compared with those of 1990, along with a five-year carbon budget plan In 2010, the DECC launched the UK 2050 Calculator to promote an energy-literate debate on how these targets can be met Since the launch, many other countries including Belgium, China, Vietnam, India, South Korea, Mexico, Indonesia, South Africa, Australia, Japan, and Thailand have also developed 2050 Pathways Calculators with assistance from the DECC The calculator - a web-based computer model - shows energy demand and supply and how these interact with the GHG emissions reduction target The calculator is based on an energy-balancing model, which used a number of sectoral trajectories to calculate possible energy pathways to 2050 The model helps one to determine what type of technology to use, and what lifestyle changes may be needed in 2050 in order to reduce emissions to national target China’s adoption and launching of its own 2050 Pathway Calculator in 2012 - just two years after the pioneers in the UK - was no surprise at all since China is the largest CO2 emitter and consumer of energy China 2050 Pathways [9], also known as “China Energy Outlook”, is China’s version of the 2050 Pathway Calculator It is now a well-known tool in the energy and climate change cycles With the help of the DECC, the China 2050 Pathways Development Group developed the calculator Although it proves to be a very good policy-making tool, the framework, assumptions and energy mixes, as well as pathways included, need further scrutiny and amendment due to complexity and errors Methods In this paper, we use both energy flow analysis and meta-analysis to critically assess the structural framework of the 2050 Pathway Calculator To this, the analysis focuses on three main areas: Total energy flow in the entire model, with emphasis on demand in transportation Key drivers or indicators of transportation and corresponding trajectories assigned Cleaner transportation as an example pathway for energy optimisation For simplicity, the key indicators of transport were coded as TDin, TMin, UGV, TDit, TMit, ITD, FTDM, FV, IFD and were assigned numbers 1, 2, 3…,9 respectively, as shown in Table There are four trajectories or scenarios for each indicator, showing the possible choices available within the pathways 277 278 Steve-Wonder Amakpah et al / Energy Procedia 104 (2016) 275 – 280 These are labeled A, B, C, and D For an example pathway, selecting B-6, B-7, B-8, C-1, C-3, C-4, C-9, D-2 and D-5 constitute Cleaner Transportation Pathway Table Key indicators and trajectories (alternative choices) for transport in China’s 2050 Pathway TRAJECTORY B C TRANSPORT INDICATOR Code Code # Inner City Transport Demand TDin A-1 B-1 C-1 D-1 Travelling Methods (Inner City) TMin A-2 B-2 C-2 D-2 Usage of Green Vehicles Inter City Transport Demand UGV TDit A-3 A-4 B-3 B-4 C-3 C-4 D-3 D-4 Travelling Methods (Inter City) TMit A-5 B-5 C-5 D-5 International Travel Demand ITD A-6 B-6 C-6 D-6 B-7 C-7 D-7 B-8 B-9 C-8 C-9 D-8 D-9 A Freight Transport Demand And Method FTDM A-7 Freight Vehicles International Freight Demand FV IFD A-8 A-9 D Results and discussions 1.1 Energy Flows in 2050 Pathways The energy composition of the model comes from all the primary energy sources of environment, wind, hydro, nuclear, coal, oil, natural gas and bioenergy-producing biomass When pathways are at the most pessimistic state (default), the supply end of the energy flow is approximately 6810Mtce At the demand end are the heating and cooling, industry, transport, agriculture, light and appliances and rural demand The total demand is 6539Mtce out of which 1991Mtce accounts for end-point loss Total oil input is 1481Mtce (81% imported) which is the primary source of energy for the transport sector Energy consumption of transportation is 1242Mtce, representing 19% of total national energy demand, with 1202Mtce (97%) coming from oil and the remaining 40Mtce supplied directly from an electricity grid We find the energy loss at the output or demand side of the energy pathways did not account for the full energy loss during transmission This is unaccounted loss that we consider as the “sink energy” 1.2 Transport Pathway What are the main options to reduce greenhouse gas (GHG) emissions in transportation? In principle, mitigation in transport can be decomposed into reducing the carbon intensity of fuels, enhancing the energy efficiency of vehicles, shifting modes, and reducing demand [10-14] The research chose to analyse transportation due to the following energy implications identified from the China 2050 Pathways: x Transportation is the highest energy consuming sector behind industry x About 90% of the total energy needed to drive the transport sector comes from imported oil x 16% of China’s CO2 emissions come from petroleum, the main energy source for transport [1] x The transport sector alone consumes 25% of the total primary energy input of China x Energy contribution from the electricity grid (40Mtce) to the transport sector is comparatively low With these unprecedented figures, China’s dream to fully translate into the Third Industrial Revolution [13] of Internet-of-Things, thus the Energy-Transportation-Communication nexus can be seen as long Steve-Wonder Amakpah et al / Energy Procedia 104 (2016) 275 – 280 overdue In the transport sector, there are four trajectories and nine indicators Using a simple arithmetic equation below, Pw = kT I - (1) Pw is the number of pathways combinations, k is a constant, T is the number of trajectories (scenarios) associated with each indicator, and I represents the number of indicators under consideration (Important note: currently, the equation only applies with the assumption that all other pathways are held at default or constant, k = 1) Therefore, there are 262,144 ways for selecting transport pathways alone, all other things being equal This large spectrum of choices – for transport alone – can make the use of the calculator cumbersome and time-consuming, taking into consideration other available choices to be made in other pathways Also, some inconsistences exist in the assumptions due to over-elaborated figures, making them unrealistic with unachievable targets Figure below demonstrates the assumptions made in each scenario (a) (b) Scenario Scenario Scenario Scenario Figure Typical transport indicators showing the four scenarios created with varied assumptions: (a) Inner City Transport Demand (KM/year) (b) Inner City Travelling Method Conclusion More than a quarter of overall global energy use is allocated to the transportation sector, causing 22% of global energy, end-use-related CO2 emission China’s contribution to global warming cannot be overemphasized, hence the 2050 Pathways Calculator comes as a timely asset and management tool for policy making To increase accuracy of prediction and promote user-friendliness of the calculator, we recommend that the Energy Flow implication and Transport Pathways parameters should be refined Therefore, we suggest the following guidelines: x All transport categories should be indicated on the energy flow chart to enable the user to know the exact energy demand by each category at the demand end, such as in the UK 2050 Pathways Calculator x The framework should account for, or indicate, the “sink energy” separately or add it to the loss x Considerable refinement should be made to the various assumptions in the trajectories to reflect achievable energy/climate change targets by 2050 x To create more public awareness, China should also develop a more interactive, animated interface of the calculator, as with the UK’s “My2050” (http://my2050.decc.gov.uk/).[16] We conclude that the framework of analysis and implicit normative assumption determines the focus of mitigating options, in terms of both descriptive emphasis and qualitative evaluation 279 280 Steve-Wonder Amakpah et al / Energy Procedia 104 (2016) 275 – 280 Acknowledgements This work is supported by the National key research and development plan (No 2016YFC0502802), Projects of Sino-America International Cooperation of NSFC (No 51661125010), the Fund for Innovative Research Group of the National Natural Science Foundation of China (Grant No 51421065), National Natural Science Foundation of China (Grant No 41471466, 71673029) References [1] US Energy Information Administration (EIA) International Energy Statistics Data, 2012 Retrieved from http://www.eia.gov/cfapps/ipdbproject/IEDIndex3.cfm?tid=90&pid=44&aid=8 [2] Chen B Energy, ecolocy and environment: a nexus perspective Energ Ecol Environ 2016; 1(1): 1-2 [3] Creutzig F Evolving Narratives of Low-Carbon Futures in Transportation Trasport Rev 2015; 36(3): 341-360 Accessed online dated10/05/2016 website; http://www.tandfonline.com/doi/pdf/10.1080/01441647.2015.1079277 [4] International Energy Agency (2012) World energy outlook 2012 International Energy Agency (IEA), OECD Retrieved from www.iea.org [5] International Energy Agency (2013) Policy pathways: A tale of renewed cities Paris: Author [6] Kahn Ribeiro S, Figueroa MJ, Creutzig F, Kobayashi S, Dubeux C, & Hupe, J Energy enduse: Transportation In The global energy assessment: Toward a more sustainable future, Laxenburg: IIASA; 2012, p.93 [7] Schafer A, Heywood JB, Jacoby HD,Waitz I Transportation in a climate-constrained world Cambridge: MIT Press; 2009 [8] Carrington D UK switch to low-carbon energyy ‘no dearer than doing nothing’.the guardian 2011 Retrieved from http://www.theguardian com/environment/2011/dec/28/uk-switch-low-carbon-energy [9] China 2050 Pathways Development Group China 2050 Pathways http://2050pathway-en.chinaenergyoutlook.org Retrieval date 10/09/2015 [10] Schipper L, Marie-Lilliu C, Gorham R Flexing the link between transport and greenhouse gas emissions Paris: International Energy Agency; 2000 [11] Creutzig F, McGlynn E, Minx J, Edenhofer O Climate policies for road transport revisited (I): Evaluation of the current framework Energy Policy 2011; 39(5), 2396–2406 doi:10.1016/j.enpol.2011.01.062 [12] Creutzig F, Kammen D M Getting the carbon out of transportation fuels Cambridge University Press 2010 Retrieved from http://www.user.tu-berlin.de/creutzig/sust.html [13] Bongardt D, Creutzig F, Huă ging H, Sakamoto K, Bakker S, Gota S, Boăhler-Baedeker S Low-carbon land transport: Policy handbook New York: Routledge; 2013 [14] Figueroa M, Lah O, Fulton L M, McKinnon A, Tiwari G Energy for transport Annual Review of Environment and Resources 2014; 39(1), 295–325 [15] Rifkin J, The third industrial revolution: How lateral power is transforming energy, the economy, and the world: St Martin's Press; 2011 [16] UK’s “My2050” Retrieved from http://my2050.decc.gov.uk/ Biography Dr Gengyuan Liu, Associate Professor, Doctoral Tutor Research interests cover Urban Ecological Planning and Waste Management, Emergy Analysis, Energy Economics and System Ecology Produced over 50 peer-reviewed papers on the journals like: Applied Energy, Science of the Total Environment, Energy Policy, and Ecological Modelling Involved in over 15 research projects ... structure of China 2050 Pathways Calculator, with focus on energy flows, measurable indicators, assumptions and energy- mix for the transportation sector Employing a hybrid energy flow- and meta -analysis, ... engine of growth to most striving economies – is still on the ascendant As of 2012, China? ??s Total Primary Energy Consumption (TPEC) and its corresponding CO2 Emission from Consumption of Energy. .. quantitative evaluation 2050 Pathways Energy Calculator – How China joined the “wagon” The 2050 Pathways Energy Calculator, also known as ? ?2050 Pathways? ?? is a generic simulation model built by the