China

What’s the Embodied Carbon in the U.S.-China Trade?

Authors: Ali Hasanbeigi, Daniel Moran

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President Donald Trump has just signed an executive order to levy tariffs on a wide range of Chinese products worth an estimated $50 billion. This will certainly have major trade implications not only between China and the U.S., but globally. Perhaps, that’s why a major sell-off is happening in global stock markets. We thought to take this opportunity to look at it from climate change perspective. Do you know what’s the embodied carbon in the trade between the U.S. and China?

In our recent study on Embodied Carbon in Globally Traded Goods funded by the ClimateWorks Foundation, Global Efficiency Intelligence, LLC. and KGM & Associate Ltd. used the most recent available data and a cutting-edge model (Eora MRIO) to conduct a global assessment of the extent of the embodied carbon in globally traded goods, so-called carbon loophole.

The graph below highlights our finding related to embodied carbon in the trade between U.S. and China in 2015. As it is illustrated, the embodied carbon in goods that U.S. imports from China is around 502 million ton of CO2, while the embodied carbon in goods China imports from the U.S. is around 67 million ton of CO2. Therefore, the net import of embodied carbon by the U.S. from China is around 435 million ton of CO2.

To put this number in perspective, the entire GHG emissions in California (the 5th largest economy in the world) in 2015 was 440 million ton of CO2.

Source: KGM & Associate and Global Efficiency Intelligence analysis   Figure. Embodied Carbon in the U.S.-China Trade in 2015 (Million ton CO2)

Source: KGM & Associate and Global Efficiency Intelligence analysis

Figure. Embodied Carbon in the U.S.-China Trade in 2015 (Million ton CO2)

It is hard, however, to quantify the carbon implication of this new U.S. tariff on imports from China without knowing the exact list of products affected and how the tariff will change the trade balance between the U.S. and China.

A tool like the U.S. tariff on imports could be good for the climate and the economy if it was based on the carbon footprint of the goods imported and was not just implemented as a blanket tariff. In fact, California recently passed the Buy Clean legislation (AB 262), which calls for the state to create rules for the procurement of infrastructure materials (steel, glass, etc.) purchased with state funds that take into account pollution levels during production. This could be an example of environmental- and climate-friendly procurement and trade tariffs that level the playing field and can benefit both industry and the environment and incentivize high polluting companies that are out-of-state or out-of-country to clean up their production in order to be able to trade with these states or countries.

Our study on Embodied Carbon of Globally Traded Goods which includes results for trade between other countries and regions of the world will be published in April 2018.

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Global Efficiency Intelligence and Rocky Mountain Institute are Assisting Chinese Cities To Peak Their GHG Emissions

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In September 2015 at the first U.S.-China Climate-Smart/Low-Carbon Cities Summit, China’s Alliance of Pioneer Peaking Cities (APPC) announced that 23 cities and provinces are now members and committed to peaking emissions by or before 2030. In addition, these cities committed to report on greenhouse gas (GHG) inventories, establish climate action plans, and enhance bilateral and multilateral partnership and cooperation. These cities and provinces represent about 16.8 percent of China’s population, 27.5 percent of national GDP, and 15.6 percent of national carbon dioxide emissions. By 2050, over 80% of Chinese population will be living in cities.

With industry sector accounting for over 65% of primary energy use and about 70% of total GHG emissions in China, it is quite common to find manufacturing plants (including heavy industries) within the boundary of many cities in China. Therefore, peaking GHG emissions in Chinese cities will not be possible without addressing the energy use and GHG emissions in industries located in those cities.

One of the APPC cities is Wuhan, the capital of Hubei province. Wuhan committed to achieve the peaking of GHG emissions by 2022. Global Efficiency Intelligence (GEI) has joined Rocky Mountain Institute (RMI) in their effort to help the city of Wuhan to peak its industrial GHG emissions by 2022. In this project, RMI and GEI are working with local partners to conduct both technical and policy analysis in order to come up with a concrete action plan and practical suggestions for the city of Wuhan to achieve its emission peaking goal. The aim is to develop methodologies and tools that can be replicated across other cities in China to help them with their GHG emissions peaking targets. 

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18 Emerging Technologies and 180 Commercialized Technologies and Measures for Energy and Water Efficiency, and GHG Emissions Reduction in the Textile Industry

The textile industry uses large amounts of electricity, fuel, and water, with corresponding greenhouse gas emissions (GHGs) and contaminated effluent.  With regard to energy use, the textile industry’s share of fuel and electricity use within the total final energy use of any one country depends on the structure of the textile industry in that country. For instance, electricity is the dominant energy source for yarn spinning whereas fuels are the major energy source for textile wet processing.

In addition to using substantial energy, textile manufacturing uses a large amount of water, particularly for wet processing of materials, and produces a significant volume of contaminated effluent. Conserving water and mitigating water pollution will also be part of the industry’s strategy to make its production processes more environmentally friendly, particularly in parts of the world where water is scarce.

In 2016, the world’s population was 7.4 billion; this number is expected to grow to 9.5 billion by 2050. The bulk of this growth will take place in underdeveloped and developing countries. As the economy in these countries improves, residents will have more purchasing power; as a result, per-capita consumption of goods, including textiles, will increase. In short, future population and economic growth will stimulate rapid increases in textile production and consumption, which, in turn, will drive significant increases in the textile industry’s absolute energy use, water use, and carbon dioxide (CO2) and other environmentally harmful emissions.

Having the higher education background in both textile technology engineering and energy efficiency technologies, I wrote a report on commercially available energy-efficiency technologies and measures for the textile industry several years ago. This report included a review of over 180 commercialized energy efficiency technologies and measures for the textile industry based on case-studies around the world. In addition to conserving energy, some of the technologies and measures presented also conserve water. The report can be downloaded from this Link (Hasanbeigi 2010).

Several other reports also document the application of commercialized technologies. However, today, given the projected continuing increase in absolute textile production, future reductions (e.g., by 2030 or 2050) in absolute energy use and CO2 emissions will require further innovation in this industry. Innovations will likely include development of different processes and materials for textile production or technologies that can economically capture and store the industry’s CO2 emissions. The development of these emerging technologies and their deployment in the market will be a key factor in the textile industry’s mid- and long-term climate change mitigation strategies.

However, information is scarce and scattered regarding emerging or advanced energy-efficiency and low-carbon technologies for the textile industry that have not yet been commercialized. That was why a few years ago, I wrote another report that consolidated available information on 18 emerging technologies for the textile industry with the goal of giving engineers, researchers, investors, textile companies, policy makers, and other interested parties easy access to a well-structured database of information on this topic. Table below shows the list of the technologies covered.

Table. Emerging energy-efficiency, water efficiency, and GHG emissions reduction technologies for the textile industry (Hasanbeigi 2015)

A few years ago when I conducted several day-long training on energy efficiency in the textile industry for hundreds of engineers and manager of textile companies in China, one major feedback we received, which did not surprise me, was that they did not know about most of the commercialized and emerging technologies we introduced. Engineers and manager are busy with day-to-day routine which rarely involves energy efficiency improvement.  

Also, you can check out the Energy Efficiency Assessment and Greenhouse Gas Emission Reduction Tool for the Textile Industry (EAGER Textile), which we developed a few years ago. EAGER Textile tool allows users to conduct a simple techno-economic analysis to evaluate the impact of selected energy efficiency measures in a textile plant by choosing the measures that they would likely introduce in a facility, or would like to evaluate for potential use.

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Some of our related publications are:

1.     Hasanbeigi, Ali; Price, Lynn; (2015). A Technical Review of Emerging Technologies for Energy and Water Efficiency and Pollution Reduction in the Textile Industry. Journal of Cleaner Production. DOI 10.1016/j.jclepro.2015.02.079.

2.     Hasanbeigi, Ali; Hasanabadi, Abdollah; Abdolrazaghi, Mohamad, (2012). Energy Intensity Analysis for Five Major Sub-Sectors of the Textile Industry. Journal of Cleaner Production 23 (2012) 186-194

3.     Hasanbeigi, Ali; Price, Lynn (2012). A Review of Energy Use and Energy Efficiency Technologies for the Textile Industry. Renewable and Sustainable Energy Reviews 16 (2012) 3648– 3665.

References:

·      Hasanbeigi, Ali (2013). Emerging Technologies for an Energy-Efficient, Water-Efficient, and Low-Pollution Textile Industry. Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL-6510E

·      Hasanbeigi, Ali, (2010). Energy Efficiency Improvement Opportunities for the Textile Industry. Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL-3970E


Structural Change in Chinese Steel Industry and Its Impact on Energy Use and GHG Emissions up to 2030

Production of iron and steel is an energy-intensive and air polluting manufacturing process. In 2014, the iron and steel industry accounted for around 28 percent of primary energy consumption of Chinese manufacturing (NBS 2015a). Steel production in 2015 was 804 Mt (worldsteel, 2016), representing 49.5% of the world production that year (Figure 1).

Figure 1. China’s Crude Steel Production and Share of Global Production (1990-2015) (EBCISIY, various years; NBS, 2015b, worldsteel 2016)

Figure 1. China’s Crude Steel Production and Share of Global Production (1990-2015) (EBCISIY, various years; NBS, 2015b, worldsteel 2016)

China is a developing country and the iron and steel industry, as a pillar industry for Chinese economic development, has grown rapidly along with the national economy. The average annual growth rate of crude steel production was around 18% between 2000 and 2010. China’s steel production in 2014 consumed around 580 TWh of electricity and 18,013 PJ of fuel (NBS 2015a).

The promotion and application of energy-saving technologies has become an important step for increasing energy efficiency and reducing energy consumption of steel enterprises, especially during the 11th Five Year Plan (FYP) (2006-2010) and 12th FYP (2011-2015). During this time, energy-efficiency technologies adopted in China’s steel industry included: Coke Dry Quenching (CDQ), Top-pressure Recovery Turbine (TRT), recycling converter gas, continuous casting, slab hot charging and hot delivery, Coal Moisture Control (CMC), and recycling waste heat from sintering. The penetration level of energy-efficiency technologies in the steel industry has improved greatly in China, improving its energy efficiency and emissions reductions (Hasanbeigi et al. 2011).

Couple of years ago, my colleagues and I conducted a study that aimed to analyze influential factors that affected the energy use of steel industry in the past in order to quantify the likely effect of those factors in the future. For the first time, we developed a decomposition analysis method that can be used for the steel industry to analyze the effect of different factors including structural change on energy use of the steel industry.

The factors we analyzed were:

  1. Activity: Represents the total crude steel production.
  2. Structure: Represents the activity share of each process route (Blast Furnace/Basic Oxygen Furnace (BF-BOF) or Electric Arc Furnace (EAF) route).
  3. Pig iron ratio: The ratio of pig iron used as feedstock in each process route. This is especially important for the EAF process because the higher the pig iron ratio in the feedstock of the EAF, the higher the energy intensity of EAF steel production.
  4. Energy intensity: Represents energy use per ton of crude steel

In that study, a bottom-up analysis of the energy use of key medium- and large-sized Chinese steel enterprises (which account for around 85% of steel production in China) was performed using data at the process level. Both retrospective and prospective analyses were conducted in order to assess the impact of factors that influence the energy use of the steel industry in the past and estimate the likely impact in the future up to 2030.

Three scenarios were developed as follows:

o   Scenario 1: Low scrap usage: the share of EAF steel production grows slower and the pig iron feed ratio in EAF drops slower than other scenarios

o   Scenario 2: Medium scrap usage: the rate of growth in the share of EAF steel production and the drop in the pig iron feed ratio in EAF production is medium (between scenario 1 and 3)

o   Scenario 3: High scrap usage: the share of EAF steel production grows faster and the pig iron feed ratio in EAF production drops faster than other scenarios.

Figure 2 shows the energy intensities calculated for different steel production route up 2030

Figure 2. Final energy intensities calculated for key medium- and large-sized Chinese steel enterprises (2000-2030)

Figure 2. Final energy intensities calculated for key medium- and large-sized Chinese steel enterprises (2000-2030)

The results of our analysis showed that although total annual crude steel production of key Chinese steel enterprises (and most likely entire Chinese steel industry) is assumed to peak in 2030 under all scenarios, total final energy use of the key Chinese steel enterprises (and most likely the entire Chinese steel industry) peaks earlier, i.e. in year 2020 under low and medium steel scrap usage scenarios and in 2015 under high scrap usage scenario (Figure 3).

Figure 3. Total final energy use in key medium- and large-sized Chinese steel enterprises under each scenario (2000-2030)

Figure 3. Total final energy use in key medium- and large-sized Chinese steel enterprises under each scenario (2000-2030)

Energy intensity reduction of the production processes and structural shift from Blast Furnace/Basic Oxygen Furnace (BF-BOF) to Electric Arc Furnace (EAF) steel production plays the most significant role in the final energy use reduction. The decomposition analysis results showed what contributed to the reduction in the final energy use and its peak under each scenario. Figure 4 shows an example of results for Medium scrap usage scenario. 

The three scenarios produced for the forward looking decomposition analysis up to 2030 showed the structural effect is negative (i.e. reducing the final energy use) during 2010-2030 because of the increase in the EAF share of steel production in this period. Similarly, the pig iron ratio effect reduces the final energy use of key steel enterprises because of reduction in the share of pig iron used as feedstock in EAF steel production during this period. High scrap usage scenario had the largest structural effect and pig iron ratio effect because of higher EAF steel production and lower pig iron use in EAFs in this scenario.

Figure 4. Medium scrap usage scenario: Results of prospective decomposition of final energy use of key medium- and large-sized Chinese steel enterprises up to 2030

Figure 4. Medium scrap usage scenario: Results of prospective decomposition of final energy use of key medium- and large-sized Chinese steel enterprises up to 2030

The intensity effect also played a significant role in reducing final energy use of steel manufacturing during 2010-2030. This is primarily because of the energy intensity assumptions for production processes in 2020 and 2030. While the realization of such energy intensity reduction is uncertain and remains to be seen in the future, the aggressive policies by the Chinese government to reduce the energy use per unit of product of the energy intensive sectors, especially the steel sector, are a promising sign that the Chinese steel industry is moving towards those energy intensity targets. The “Top-10,000 Enterprises Energy Saving Program” and the “10 Key Energy Saving Projects Program” along with other policies and incentives in the coming years will significantly help to reduce the energy intensity of the steel industry in China.

More details of our analysis and results are presented in our report that is published on LBNL’s website and can be downloaded from this Link.

Please feel free to contact me if you have any question. Don't forget to follow us on LinkedInFacebook, and Twitter to get the latest about our new blog posts, projects, and publications.

Some of our related publications are:

  1. Hasanbeigi, Ali; Arens, Marlene; Rojas-Cardenas, Jose; Price, Lynn; Triolo, Ryan. (2016). Comparison of Carbon Dioxide Emissions Intensity of Steel Industry in China, Germany, Mexico, and the United States. Resources, Conservation and Recycling. Volume 113, October 2016, Pages 127–139
  2. Zhang, Qi; Hasanbeigi, Ali; Price, Lynn; Lu, Hongyou; Arens, Marlen (2016).  A Bottom-up Energy Efficiency Improvement Roadmap for China’s Iron and Steel Industry up to 2050. Berkeley, CA: Lawrence Berkeley National Laboratory. LBNL- 1006356
  3. Morrow, William; Hasanbeigi, Ali; Sathaye, Jayant; Xu, Tengfang. 2014. Assessment of Energy Efficiency Improvement and CO2 Emission Reduction Potentials in India’s Cement and Iron & Steel Industries. Journal of Cleaner Production. Volume 65, 15 February 2014, Pages 131–141
  4. Hasanbeigi, Ali; Price, Lynn, Aden, Nathaniel; Zhang Chunxia; Li Xiuping; Shangguan Fangqin. 2014. Comparison of Iron and Steel Production Energy Use and Energy Intensity in China and the U.S. Journal of Cleaner Production, Volume 65, 15 February 2014, Pages 108–119
  5. Hasanbeigi, Ali; Morrow, William; Sathaye, Jayant; Masanet, Eric; Xu, Tengfang. (2013). A Bottom-Up Model to Estimate the Energy Efficiency Improvement and CO2 Emission Reduction Potentials in the Chinese Iron and Steel Industry. Energy, Volume 50, 1 February 2013, Pages 315-325
  6. Hasanbeigi, Ali; Arens, Marlene; Price, Lynn; (2013). Emerging Energy Efficiency and CO2 Emissions Reduction Technologies for the Iron and Steel Industry. Berkeley, CA: Lawrence Berkeley National Laboratory BNL-6106E.

 

References

Editorial Board of China Iron and Steel Industry Yearbook (EBCISIY). Various years. China Iron and Steel Industry Yearbook. Beijing, China (in Chinese).

Hasanbeigi, A., Price, L., Aden, N., Zhang C., Li X., Shangguan F. 2011. A Comparison of Iron and Steel Production Energy Use and Energy Intensity in China and the U.S. Berkeley CA: Lawrence Berkeley National Laboratory Report LBNL-4836E.

NBS. 2015a. China Energy Statistics Yearbook 2015. Beijing: China Statistics Press.

NBS. 2015b. China Statistical Yearbook 2015. Beijing: China Statistics Press.

World Steel Association (worldsteel). 2016. Steel Statistical Yearbook 2016. 


Moving Beyond Equipment and to System Efficiency: Massive Energy Efficiency Potential in Industrial Steam Systems in China

Author: Ali Hasanbeigi, Ph.D.

China is responsible for nearly 20% of global energy use and 25% of global energy-related CO2 emissions. The industrial sector dominates the country’s total energy consumption, accounting for about 70% of primary energy use and also country’s CO2 emissions. For these reasons, the development path of China’s industrial sector will greatly affect future energy demand and dynamics of not only China, but the entire world.

Sources: NBS, China Energy Statistical Yearbooks 2015. EIA, 2015

Sources: NBS, China Energy Statistical Yearbooks 2015. EIA, 2015

Steam is used extensively as a means of delivering energy to industrial processes. On average, industrial boiler and steam systems account for around 30% of manufacturing industry energy use worldwide. There exists a significant potential for energy efficiency improvement in steam systems; however, this potential is largely unrealized. A major barrier to effective policymaking, and to more global acceptance of the energy efficiency potential of steam systems, is the lack of a transparent methodology for quantifying steam system energy efficiency potential based on sufficient data to document the magnitude and cost-effectiveness of these energy savings by country and by region.

Source: U.S. DOE/AMO, 2012

Source: U.S. DOE/AMO, 2012

In 2013-2014, I led a UNIDO-funded study to develop and apply a steam system energy efficiency cost curve modeling framework to quantify the energy saving potential and associated costs of implementation of an array of boiler and steam system optimization measures. The developed steam systems energy efficiency cost curve modeling framework was used to evaluate the energy efficiency potential of coal-fired boiler (around 83% of industrial boilers) and steam systems in China’s industrial sector. Nine energy-efficiency technologies and measures for steam systems are analyzed.

The study found that total cost-effective (i.e. the cost of saving a unit of energy is lower than purchasing a unit of energy) and technically feasible fuel savings potential in industrial coal-fired steam systems in China in 2012 was 1,687 PJ and 2,047 PJ, respectively. These account for 23% and 28% of the total fuel used in industrial coal-fired steam systems in China in that year, respectively. The CO2 emission reduction potential associated with the cost-effective and total technical potential is equal to 165.82 MtCO2 and 201.23 MtCO2, respectively. By comparison, the calculated technical fuel saving potential for industrial coal-fired steam systems in China is approximately 9% of the total coal plus coke used in Chinese manufacturing in 2012 and is greater than the total primary energy use of over 160 countries in the world in 2010.

Several sensitivity analyses were conducted, their policy implications discussed, and uncertainties and limitations of this study were presented in the report we published. Our report is published by UNIDO and can be downloaded from here. Please feel free to contact me if you have any question.

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