Embodied energy of service trading in Hong Kong

Purpose Energy is a resource of strategic importance for high density cities. International trade reshapes the urban economy and industrial structure of a city, which will indirectly affect energy use. As an international trade hub, Hong Kong relies on the import and export of services. Energy performance in the international trading of these services needs to be properly understood and assessed for Hong Kong’s urban renewal efforts. Design/methodology/approach This study evaluates Hong Kong’s embodied energy in service trades based on an input-output analysis. The three criteria used for assessment include trading areas, industry sector, and trade balance. Findings Analyzed by region, results show that Mainland China and the USA are the two largest sources of embodied energy in imports of services, while Mainland China and Japan are the two largest destinations of exports. In terms of net embodied energy transfer, Hong Kong mainly receives net energy import from Mainland China and the USA and supplies net energy export to Japan, the UK and Taiwan. Among industry sectors, Manufacturing services, Transport and Travel contribute most significantly to the embodied energy in Hong Kong’s imported services, while Transport and Travel contribute most to the energy embodied in exported services. Originality/value This study identifies the characteristics of energy consumption of service trading and establishes a feasible approach to analyze energy performance of service trade in energy-deficient Hong Kong for the first time. It provides necessary understanding and foundation for developing energy strategies in a service-based, high density urban economy

Multi-scale input-output analysis of consumption-based water resources: Method and application

This work develops a method of multi-scale input-output analysis for the embodied water accounting of an economy. This method can distinguish between the different virtual water contents of imported and local products and is therefore capable of accurately estimating the virtual water that is embodied in trade. As a simplified model rather than a multi-regional input-output analysis, this method substantially minimizes the data requirements. With the support of averaged Eora global embodied water intensity databases for the world and Chinese economies, a three-scale embodied water input-output analysis of the Beijing economy in 2007 has been conducted. Dozens of virtual water flows that relate to the Beijing economy have been identified and analyzed. Only 15% of the total water resources embodied in Beijing's local final demand were from local water withdrawal; 85% were from domestically and internationally imported products. The virtual water import is revealed to play a more important role than physical water transfer in easing Beijing's water shortage. Since the average water use efficiency of the Beijing economy is much higher than that of the Chinese economy but somewhat lower that of the rest of the world, Beijing is suggested to shifting its imports to foreign countries to optimize global water use. The method developed can be useful for water saving strategies for multiple responsible entities holding different opinions, and it can be easily applied to the embodied water accounting of a sub-national or even smaller economic community

Greenhouse Gas Inventory Accounting for Chinese Cities: A Preliminary Study

City Greenhouse Gas (GHG) inventory, a framework for measuring a city’s detailed emissions from all activities, provides scientific evidence for the purpose of policy-making. As one of the largest GHG emitters in the world, China aims to reduce CO2 emissions per unit of GDP to 60 to 65 percent below 2005 levels by 2030. However, city GHG inventories in China have not yet been published by the city governments. Furthermore, previous studies on city inventory accounting are neither complete nor globally comparable. Hence, a case study of Beijing was conducted for the purpose of reporting the city inventory completely and enabling data to be comparable internationally. This research quantifies Beijing’s latest emissions based on available data through multiple methods, including Community-Scale Greenhouse Gas emissions inventories (GPC), a method devised by the Japanese Ministry of Environment (Japanese Ministry of Environment, 2010) and a method from recent academic research on CO2 emissions in the Chinese iron and steel industry (Zhao, Y. Q., Li, & Li, 2012). According to these methods, Beijing’s GHG emissions were 373,558,617 t CO2 in 2012. Additionally, comparisons between Beijing and six other mega-cities of Shanghai, Tokyo, New York, Washington D.C., London and Paris show that Beijing’s 2012 GHG emission per capita and per 10,000 CNY GDP ranked the highest. This study creates a timely and relatively complete GHG emission inventory that can be widely applied for comparisons and presents recommendations for city inventory building

CO2 emission based GDP prediction using intuitionistic fuzzy transfer learning

The industrialization has been the primary cause of the economic boom in almost all countries. However, this happened at the cost of the environment, as industrialization also caused carbon emissions to increase exponentially. According to the established literature, Gross Domestic Product (GDP) is related to carbon emissions (CO2) which could be optimally employed to precisely estimate a country's GDP. However, the scarcity of data is a significant bottleneck that could be handled using transfer learning (TL) which uses previously learned information to resolve new tasks, more specifically, related tasks. Notably, TL is highly vulnerable to performance degradation due to the deficiency of suitable information and hesitancy in decision-making. Therefore, this paper proposes ‘Intuitionistic Fuzzy Transfer Learning (IFTL)’, which is trained to use CO2 emission data of developed nations and is tested for its prediction of GDP in a developing nation. IFTL exploits the concepts of intuitionistic fuzzy sets (IFSs) and a newly introduced function called the modified Hausdorff distance function. The proposed IFTL is investigated to demonstrate its actual capabilities for TL in modeling hesitancy. To further emphasize the role of hesitancy modelled with IFSs, we propose an ordinary fuzzy set (FS) based transfer learning. The prediction accuracy of the IFTL is further compared with widely used machine learning approaches, extreme learning machines, support vector regression, and generalized regression neural networks. It is observed that IFTL capably ensured significant improvements in the prediction accuracy over other existing approaches whenever training and testing data have huge data distribution differences. Moreover, the proposed IFTL is deterministic in nature and presents a novel way for mathematically computing the intuitionistic hesitation degree.© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Modeling combined role of renewable electricity output, environmental regulations, and coal consumption in ecological sustainability

The Sustainable Development Goals (SDGs) allow global economies to establish nationally determined plans to track ecological and economic sustainability at regional and international levels. While renewables are proven effective ecological sustainability tools, they might not comprehensively achieve climate change mitigation without emissions control regulations available in the combined policy toolbox, largely ignored by mainstream research. Therefore, we empirically assess the combined effect of renewable electricity output, stringent envi-ronmental regulations, and coal consumption in an attempt to acquire sustainable ecosystems via employing second-generation methodological approaches to ten selected OECD members' data during 1990-2015. We revealed long-term equilibrium among our study variables, implying that our variables maintain inherent sta-bility. Also, the environmental Kuznets Curve notion is verified in the long term. Notably, we found that renewable energy output and stringent environmental regulations effectively mitigate ecological footprint and carbon emissions, whereas coal consumption boosts them. However, as the degree of coal consumption-driven carbon emissions promotion impact exceeded that of the emissions reduction impact of renewable energy output, simply ramping up the deployment of renewable energy solutions would be insufficient for climate change mitigation targets. Instead, a comprehensive climate policy inclusive of energy transformation and environmental regulations would be indispensable. While all the under-analysis variables unveil significant impacts merely in the long term, their respective short-term policies would be ineffective. We suggest imple-menting marketable and non-marketable environmental laws and deploying renewable solutions for achieving SDGs involving climate action and universal access to affordable alternative energy to guide a green and sustainable future

Methods to assess the impacts of subnational sustainability

Environmental, social, and economic problems, such as global warming, natural disasters, urbanization, and poverty, are interlinked and become more complexly entwined under globalization. In 2015, recognizing global problems such as these, all United Nations member states adopted the 2030 Agenda for Sustainable Development, which outlines sustainable development goals (SDGs). At the same time, the United Nations Framework Convention on Climate Change (UNFCCC) adopted the Paris Agreement, which has the long-term goal of mitigating greenhouse gas (GHG) emissions; each nation is expected to increase its mitigation target in order to limit global warming to less than 2 degrees Celsius. To achieve the goals set out in the international agreements, nations need to identify problems and assess the impact of these problems at the subnational level, not only on a national and worldwide scale. In fact, there is an ever-growing need to construct a subnational analysis tool to identify problems and find solutions using micro- and macro-analytical tools, such as a multiregional input–output (MRIO) database. The main aim of this thesis is to develop models for sustainability analysis at the subnational level and apply them to assessing environmental, economic, and social impacts. To this end, a cloud-computing platform called the Japan Industrial Ecology Laboratory (IELab) was developed. The IELab is highly flexible in terms of its sectoral and regional resolution—enabling users to build customized Japanese MRIO tables in accordance with their specific objectives. A subnational MRIO analysis can track inter-regional trade for cities, counties, or states within a country. Footprint analysis conducted using the MRIO database can help fill in information gaps between producers and consumers on various economic, social, and environmental issues. In the case study, food loss analysis was conducted to examine regional food loss, not only from a production perspective, but also from a demand-side. As another subnational analytical method, a bottom-up technology model was presented as CO2 emission mitigation as an example. Using the model, the impact of future technological changes in the regional electricity system on Japan’s overall energy mix was assessed