TECHNICAL NOTE | December 2019 | 1 TECHNICAL NOTE Technical notes document the research or analytical ology underpinning a publication, interactive application, or tool. Suggested Citation Xiaoqian Jiang, Mengpin Ge, Robbie Orvis, Jeffrey Rissman, Lawrence Iu, and Roman Henning. 2019. “Hong Kong Energy Policy Simulator s, Data, and Scenario Results for 2050.” Technical Note. World Resources Institute, Beijing. Available online at http// XIAOQIAN JIANG, MENGPIN GE, ROBBIE ORVIS, JEFFREY RISSMAN, LAWRENCE IU, AND ROMAN HENNING HONG KONG ENERGY POLICY SIMULATOR S, DATA, AND SCENARIO RESULTS FOR 2050 CUTIVE SUMMARY The government of Hong Kong published the Hong Kong’s Climate Action Plan 2030 report in 2017 as a response to the Paris Agreement. The report states Hong Kong’s carbon emission reduction target for 2030 and outlines action plans to meet it. Hong Kong has pledged to reduce 65 to 70 percent of its carbon intensity per gross domestic product GDP by 2030, using 2005 as the base. This is equivalent to a 26 to 36 percent reduction from 2005 emissions levels and an expected reduction to 3.3–3.8 tonnes on a per capita basis, compared with 6 tonnes per capita in 2005. While not a party to the Paris Agreement itself, Hong Kong as a Special Administrative Region of China contributes to the fulfillment of China’s Nationally Determined Contribution of the Paris Agreement. Hong Kong similarly plays a part in contributing to meeting the long-term target of the Paris Agreement to limit global temperature increase to less than 1.5 to 2 degrees Celsius. Hong Kong currently does not have a long- term decarbonization strategy or target beyond 2030 in place. Thus, it is critical for Hong Kong to ulate a long-term decarbonization plan to which the short- and medium-term actions could con. In order to inspire ambition and mobilize action to advance Hong Kong through a cross-sectoral transition CONTENTS cutive Summary . 1 Introduction 2 Background on the Energy Policy Simulator 3 Data Sources 7 Scenarios and Results . 8 Online Hong Kong Energy Policy Simulator . 16 Limitations . 17 Future Development 18 Appendix. Data Sources for Variables in the Hong Kong EPS . 18 Endnotes . 28 References 292 | toward long-term deep decarbonization, World Resources Institute WRI and Civic Exchange CE jointly initiated the project “Hong Kong 2050 Is Now.” Under this project, WRI and CE collaborated with Energy Innovation LLC to develop the Hong Kong Energy Policy Simulator EPS, which aims to provide technical support for developing Hong Kong’s 2050 deep decarbonization strategy. The “Hong Kong 2050 Is Now” project provides policy recommendations based on the results of the Hong Kong EPS. Meanwhile, the Hong Kong EPS also allows users to create their own scenarios to see impacts of different policy combinations. This technical note describes the structure, data sources, outputs, limitations, and future development, as well as the online interface, of the Hong Kong EPS. A subsequent policy report will present the model’s results and policy recommendations in more detail. INTRODUCTION The Paris Agreement, entered into force in November 2016 in advance of the 22nd Conference of the Parties COP 22, brings together nations of the world to put forward determined efforts to reduce greenhouse gas GHG emissions, and to adapt to the effects of climate change. The Hong Kong Special Administrative Region SAR of China hereinafter referred to as Hong Kong is covered under China’s Nationally Determined Contribution NDC of the Paris Agreement and contributes to its fulfillment. In 2010, Hong Kong announced its initial target to reduce its carbon intensity by 50 to 60 percent from 2005 levels before 2020 Environment Bureau 2015b. 1In 2017, the Hong Kong government published Hong Kong’s Climate Action Plan 2030 report in response to the Paris Agreement. The report states the government’s carbon emissions reduction target for 2030 and outlines action plans to meet it. Hong Kong has pledged to reduce 65 to 70 percent of its carbon intensity by 2030, using 2005 as the base. This is equivalent to a 26 to 36 percent absolute reduction and a reduction to 3.3–3.8 tonnes on a per capita basis, compared with 6 tonnes per capita in 2005 Environment Bureau 2017. 2In addition to these climate targets, the Hong Kong government made corresponding targets in the areas of energy saving, energy efficiency, electricity generation fuel mix, buildings, transportation, and waste management. These targets contribute to the achievement of climate change objectives. For example, Hong Kong is attempting to reduce energy intensity by 40 percent of its 2005 level before 2025 Environment Bureau 2015a, 3reduce the share of coal in the power generation sector to 25 percent, and increase the share of natural gas to 50 percent by 2020. Through 2030, coal will continue to be phased out while the use of natural gas and non-fossil fuels will be increasing Environment Bureau 2017. 4Government buildings are targeted to decrease energy consumption by 5 percent between 2015 and 2020 Government of Hong Kong 2015. 5There are also targets to cut down the amount of food waste that goes to landfills by at least 40 percent before 2022 and to reduce the municipal solid waste disposal rate to landfills by 40 percent on a per capita basis by 2022, relative to 2011 numbers. Environment Bureau 2014; Environment Bureau 2013. 6While plentiful, these policy initiatives are designed for the short and midterm. A key aspect of the Paris Agreement is to strengthen global efforts to meet the long-term target of limiting the global temperature increase to below 1.5 - 2 degrees Celsius. However, Hong Kong currently does not have a long-term decarbonization strategy or target beyond 2030. ulation of long-term low GHG development plan is crucial for Hong Kong in planning a path toward decarbonization, as well as to guide its short- and mid-term actions to be in line with the global deep decarbonization target. In order to inspire ambition and mobilize action to advance Hong Kong through a cross-sectoral transition toward long-term deep decarbonization, World Resources Institute WRI and Civic Exchange CE jointly initiated a project called “Hong Kong 2050 Is Now.” Under this project, WRI and CE collaborated with Energy Innovation LLC to develop the Hong Kong Energy Policy Simulator EPS, which aims to provide technical support for developing Hong Kong’s 2050 deep decarbonization strategy. The “Hong Kong 2050 Is Now” project provides policy recommendations based on the results of the Hong Kong EPS. Meanwhile, the Hong Kong EPS also allows users to create their own scenarios to see impacts of different policy combinations. This technical note discusses the structure, function, data, and output specific to the Hong Kong EPS. A complementing policy report will introduce more detailed analysis and policy recommendations.Hong Kong Energy Policy Simulator s, Data, and Scenario Results for 2050 TECHNICAL NOTE | December 2019 | 3 BACKGROUND ON THE ENERGY POLICY SIMULATOR About the Hong Kong Energy Policy Simulator The Hong Kong Energy EPS is a version of the Energy Policy Simulator, https//www.energypolicy.solutions/, an open source, system-dynamics computer model. The EPS is able to estimate the effects of various policies on many indicators, such as emissions, financial metrics, electricity system structure, deployment of different types of vehicles, as well as many other data. 7The EPS simulates energy policies as well as non-energy policies, such as those affecting industrial processes. EPS policies are actions taken, not targets. The EPS is generally a forward-simulating, not goal-seeking, model. Therefore, policies generally constitute specific actions or measures that influence actions, such as changing the price of something, rather than specifying targets to be met via unknown actions. The tool allows users to explore various policy combinations to create policy scenarios, including custom policy implementation schedules. The EPS simulates the years 2017 to 2050 by using annual time steps and offers hundreds of environmental, economic, and social outputs. Significant output indicators include emissions of 12 pollutants, 8cash flows first- order costs and savings to government, consumer or labor, non-energy industries, and each of the energy industries, 9capacity and generation of electricity by different types of power plants, market share of different vehicle technologies, and premature deaths avoided by reductions in particulate emissions. These output metrics can help policymakers anticipate long- term economic impacts and costs of implementing new policies. Some of the policies included in the EPS have not yet been explored in Hong Kong, thus offering novel options to policymakers. More detail on the technical aspects of the Hong Kong EPS is available in the EPS online documentation at https// hongkong.energypolicy.solutions/docs/. The model is free and open source. It can be used via an interactive web interface at https//hongkong.energypolicy.solutions/ or can be downloaded from the same site. Previous adaptation of EPS in other regions is introduced in Box 1. Why Use a Computer Model Before considering the structure and uses of the Hong Kong EPS, it is worthwhile to ask, “Why should we use a computer model at all” A policymaker seeking to reduce emissions faces a dizzying array of policy options that might advance policy goals. Policies may be specific to one sector or type of technology for instance, light-duty vehicle fuel economy standards or might be economy-wide such as a carbon tax. Sometimes a market-driven approach or a direct regulatory approach or a combination of the two can be used to advance the same goal. For instance, to improve EPS has been successfully applied in many other regions, including Canada, the Alberta Province of Canada, China, India, Indonesia, Mexico, Poland, Saudi Arabia, and the United States. All the models can be found at https//www.energypolicy.solutions/. WRI contributed to the development of the India Mangan et al. 2019, Indonesia Rissman and Chrysolite 2017, and Mexico models Altamirano et al. 2016. The relevant technical note and working paper can be found on WRI’s website ● Achieving Mexico’s Climate Goals An Eight-Point Action Plan https//www.wri.org/publication/achieving-mexicos-goals ● A Tool for Designing a Policy Package to Achieve Indonesia’s Climate Targets Summary of s and Data Used in the Indonesia Energy Policy Simulator https//www.wri.org/publication/indonesia-eps-tech-note ● A Tool for Designing Policies to Achieve India’s Climate Targets Summary of s and Data Used in the India Energy Policy Simulator https//www.wri.org/publication/achieve-india-climate-targets Box 1 | Previous Adaptation of EPS in Other Regions4 | the efficiency of home appliances, a government may offer rebates to buyers of efficient models or mandate that the appliance manufacturers meet specific energy- efficiency standards or both. To navigate this field of options, policymakers require an objective, quantitative mechanism to determine which policies will meet their goals and at what cost. Many studies have examined certain energy policies in isolation. However, it is of greater value to policymakers to understand the effects of a package of different policies because the policies may interact nonlinearly. This interaction among policies can produce results different from the sum of the effects of the individual policies. For example, a policy that promotes energy efficiency in addition to a policy that reduces the cost of wind energy, enacted together, are likely to reduce emissions by a smaller amount than the predicted sum of each of those two policies enacted separately. This is because some of the electricity demand that was eliminated by the efficiency policy would otherwise have been supplied by additional zero-emissions wind generation caused by the wind policy. In this case, the total effect is less than the sum of the individual effects. The opposite is also possible. For example, policies that promote the electrification of light-duty vehicles in addition to making wind energy cheaper are likely to have a greater impact on emissions together than the sum of these policies’ individual effects. Due to the strength of integrated computer models at simulating complex systems, a customized integrated dynamic computer model is a crucial tool to help Hong Kong policymakers uate a wide array of policies. A satisfactory model must be able to represent the entire economy and energy system with an appropriate level of disaggregation, be easy to adapt to represent Hong Kong, be capable of computing a wide array of relevant policy options, and offer results that include a variety of policy- relevant outputs. Additionally, the model must capture the interactions of policies and other forces in a system the parameters of which change dramatically over the course of the model run as Hong Kong continues to grow and develop. About System Dynamics Modeling Many approaches exist for representing the economy and the energy system in a computer simulation. The Energy Policy Simulator is based on a theoretical framework called “system dynamics.” As the name suggests, this approach views the processes of energy use and the economy as an open and fluctuating non-equilibrium system. System dynamics models often include stocks or variables the values of which are affected by flows in and out of these variables. For example, a stock might be the total installed capacity of wind power plants, which can only grow or shrink gradually from the construction of new turbines an inflow and retirement of old turbines an outflow. In contrast, the amount of energy generated by wind turbines in a given year is calculated afresh every year based on the installed capacity in that year and therefore is a normal variable, not a stock variable. The Energy Policy Simulator uses stock variables for two purposes ▪ Tracking quantities that grow or shrink over time such as the total wind electricity generation capacity ▪ Tracking differences from the current policy scenario data that are prone to change over the course of the model run for instance, the cumulative differ- ences caused by enabled policies in the potential fuel consumption of the light-duty vehicle fleet System dynamics models often use the output of the previous timestep’s calculations as for the following timestep. 10The Energy Policy Simulator follows this convention, with stocks like the electricity generation fleet, the types and efficiencies of building components, and other stocks recorded from one year to the next. Therefore, an efficiency improvement in a prior year w