Corporate Average Fuel Consumption and New Energy Vehicles Credits Joint Management Method Draft II (for public consultation)
This second draft regulation proposed by the Ministry of Industry and Information Technology (MIIT) and released by the Law Department of the State Council takes into account comments provided since its September 2016 release, including its WTO consultation. Additional comments will be submitted on June 27 and incorporated into a subsequent draft. We've prepared a quick review of the new draft, and gathered some comments we will submit to MIIT soon. Welcome your thoughts and suggestions!
ICE vehicle energy efficiency improvements should be highlighted along with NEV in pursuit of CAFC target
According to the MIIT, 2016 Corporate Average Fuel Consumption (CAFC) memo released on April 2017 , China’s market reached an average of 6.56L/100km, comprised of domestic manufacturers with 6.51L/100km and imported with an average CAFC of 7.89L/100km. In comparison to last year’s average of 7.04L/100km, this is a drop of 7% in corporate average fuel efficiency. Also against the 2016 target of 6.7L/100km, China’s corporate auto players performed well. However, a more thorough investigation reveals issues surrounding the actual energy efficient technology improvements of China’s huge ICE vehicle fleet. This brief introduces the highlights of our coming 2017 CAFC analysis.
Online Ride-Hailing Network and Fuel Consumption: A Driver’s Perspective (February 2017/ Clean Transportation Program)
The online ride-hailing network has transformed China’s urban mobility and has the potential to deliver significant air quality improvements and advance urban smart mobility. However, the environmental pros and cons of the network have not yet been thoroughly investigated and the information about actual trips and design of the network operation has largely remained in the private sector’s domain. Prior to the pursuant of “hard” data which require close interactions with operators and government, this study demonstrates that easily accessible “soft” data such as qualitative interviews can be collected for guiding the design of future research, such as surveys, data sorting, and data compiling.
iCET's Clean Transportation Transformation Program's (CTTP) mission is to dramatically reduce fossil energy use and carbon emissions, bring back blue sky, and promote sustainable mobility through intelligent decision making by consumers and decision-makers enabled by sound scientific information and big data analytics. In 2016 CTTP generated 9 study reports, 5 policy briefs, 1 urban transport emissions calculation tool, held 10 events among which are a side event at the US-China Climate Leaders' Summit, and presented its work outcomes in 12 international and national events inducing BAQ2016, Bloomberg Future New Energy Summit and 10th anniversary of China's Ministry of Transportation's CUSTReC. Welcome to review this easy-read!
Urban Transportation Emissions Using Uber Data: A Chengdu Feasibility Study
The goal of this novel Uber feasibility case study is of two-folds: (i) to demonstrate that the use of ICT sources for gathering data relevant for urban transport emissions and policy is of value both because it is valid and it is relatively resource-efficient. The study does so by employing ICT-inducted trip data gathered and provided (for free) by Uber Chengdu; (ii) to estimate real-world carbon emissions using Uber data in combination with an urban emissions model (COPERT), and assess gaps between reported fuel consumption and actual fuel consumption.
China Passenger Vehicle Fuel Consumption Development Annual Report 2016 (January 2017/ Clean Transportation Program)
iCET's "2016 China Passenger Vehicle Fuel Consumption Development Annual Report" – the sixth report of its kind – analyzes the gaps of Phase III and IV of China's fuel consumption standard based on China's 2015 reported fuel consumption (FC) data and production of each auto manufactures , presents auto manufactures' individual FC performance, evaluates New Energy Vehicles' (NEVs) contribution to corporate and overall car market performance, and proposes recommendations towards the 2020 target of 5L/100km and translates to CO2 emissions of 167kg/km (from the 2015 target of 6.9L/100km or about CO2 120kg/km).
This study aims to assess the gap between reported and real-world fuel consumption (FC). It therefore uses the reported FC data available on the MIIT’s website and a bottom-up actual FC data collection App, BearOil App which includes nearly 600,000 owners and over 15 million data inputs inserted between 2008 and 2015, covering 16,000 vehicle models in 31 cities in China. By-segment, by-brand, by-model year, and by-transmission FC gaps are analyzed with simple possible reasons explanation. Last but not least, this report further highlights the need for independent and accountable third-party scrutiny of auto standards implementation status
2016 Green Car China Annual Report (August 2016 / Clean Transportation Program)
This year’s Green Car China Annual Report released green rankings for 10 vehicle categories (including plug-in hybrid ranking for the first time), as well as top best-selling models’ ranking. Blue Score was used to evaluate the emission level instead of Smog Index in this report. Additionally, average Green Scores and Blue Scores of the past 3 years were analyzed and compared to show the trend of vehicles’ environmental performance.
2016 China Green Car Finalists (August 2016 / Clean Transportation Program)
This brochure showed to consumers the Top 10/5 rankings for ten vehicle categories, including Small, Compact, Mid-size, Large, SUV, MPV, Luxury, Sports and PHEV (PHEV was ranked for the first time). Green Score, Blue Score, and GCC Rating were illustrated in the ranking, aiming to encourage consumers to choose vehicle models with less environmental impacts. Green Car China Ranking has been supported by Energy Foundation (financially), UNEP and MEP-VECC (technically) and entered its 7th year since public release.
The BestEV v1.0 methodology summary brief is meant to provide a quick overview of the process and results of each of the BestEV methodology development stage and the final result. The brief also includes the calculation method or arriving at the BestEV's v1.0 qualitative and quantitative criteria, as well as an end results divided to vehicle cost ranges.