http://www-iam.nies.go.jp/aim/

Research Activities on Local
Decarbonization in Japan:
Progress and Way Forward

http://www.nies.go.jp/Shuichi Ashina

Head of Decarbonization Measures Assessment Section,Social Systems Division,National Institute for Environmental Studies (NIES)
E-mail: ashina.shuichi@nies.go.jp

The presentation includes research outcomes by Environment Research and Technology Development Fund (ERTDF, 1-2003,1-2305) of Environmental Restoration and Conservation Agency and Ministry of the Environment, Japan, Decarbonized andSustainable Society Research Program and Co-design Approach for Local Sustainability Research Program (Co-SUS) of NIES,and “Development of Regional Planning System of Green Innovation for a Decarbonized Society and Establishment of Social
Implementation Network through Regional Coalition Action” of Ministry of Education, Culture, Sports, Science and Technology(MEXT), Japan.
thThe 29 AIM International Workshop
Session 2: GHG mitigations in Asia (1)14 September, 2023 at Ohyama Memorial Hall of NIES2

2023/09/14

Feasibility Study of a Carbon Neutral Society in 2050

These municipalities has largescale chemical and steel
industries.

Number of municipalities(cumulative total from 100% reduction (net-zero))

Number of municipalities in each range of reduction

 We have developed simple but AIM/Enduse-like model, andapplied to feasibility study of net-zero for all municipalities in Japan.
 Taking measures set in AIM’s net-zero scenario for Japan, morethan 90% of municipalities will achieve a CO2 reduction over
90% from 2013 level.
From presentation in the 28th AIM Int’l WS

Ref: Ashina, S. (2023) Feasibility Study of a Carbon Neutral Society in 2050 at Local Scale in Japan [in Japanese], ChikyuKankyo, 27 (2), 137-146.

2023/09/14

Research Progress in the year 2023
Supply3 Estimation of renewable energy potential inconsideration of degradation of biodiversity
 Design of interregional transmission networkof renewable energies among country Pathways toward Local Decarbonization inJapan Application of Artificial Intelligence (AI) andMachine Learning (ML) to analysis of electricity
consumption data

Demand Development of electricity monitoring andpower billing system in NIES

4

2023/09/14

Decarbonization requires large penetration of renewables
 Net-zero emissions in Japan require a major shift from fossil fuels,
currently 80% of primary energy supply, to decarbonized energy,
especially to renewable energy of ~70% by 2050.
(Mtoe)

Primary Energy Consumption
Alternative fuels
Renewables
Biomass
Nuclear power
Gas
OilCoalEnergy self-sufficiency rate

NDC+

TECH TECH+SC

Renewables andnuclear power

Modified based on https://www-iam.nies.go.jp/aim/projects_activities/prov/2023_2050ep/230428_2050年脱炭素社会実現に向けた排出経路分析rev230627.pdf

52023/09/14

Conflicts between renewables and local communities Large-scale renewable penetration may cause conflicts withlocal communities – cause of mudslides, birds/bats and windturbines, and degradation of natural resources.
Mudslides and PVs

Source: Ministry of Economy, Trade and Industry (METI),
https://www.meti.go.jp/shingikai/sankoshin/hoan_shohi/denryoku_anzen/newenergy_hatsuden_wg/pdf/014_01_00.pdf

PVs on Wetlands in Hokkaido

Source: https://kumamori.org/topics/kumamorinews/20221223_1.html

62023/09/14

Protection of biodiversity and renewable potentials
 Satoyama Index (SI)* is used as a level of biodiversity. High SI
means the area has high heterogeneity of land use.
 Exclude points with a SI value of 0.3 or higher will reduce thetotal potential to 1,070 TWh, around 70% of baseline case.
Changes in PV potentials (Total over Japan, GWh/yr)
Baseline(None)

ExcludesSI >= 0.3

*More detailed about Satoyama Index:

Yoshioka, A., Kadoya, T., Imai, J., and Washitani. I. (2013) Overview of landuse pattern of Japanese Archipelago with biodiverisity-conscious landuse classification and Satoyama Index. Japanese Journal of Conservation Ecology 18:141-156
Kadoya T., and Washitani I. (2011) The Satoyama Index: a biodiversity indicator for agricultural landscapes. Agriculture, Ecosystems and Environment, 140: 20-26.

2023/09/14

7

Simplified Excel Tool for Local Decarbonization Scenario
 Simplified excel tool is developed for local decarbonization
scenario analysis assuming use by officials and policymakers.
 The tool applied to all (1,741) municipalities in Japan.
Snapshots of the tool (for Tsukuba city)
No Action Case

Calculation flow in the tool
Energy balance table(baseyear, by municipality)(1) Baseline change(Only includes socio-economic changes)(2) Demand Reductions(Lifestyle changes etc)

Decarbonization Case

(3) Efficiency Improvement(Installation of EE technologies)(4) Fuel shifts in demand side
(Electrification, alternative fuels)(5) Fuel shifts in supply side
(Renewables, alternative fuels)

82023/09/14

Example of Pathways toward Local Decarbonization
 Taking actions set in AIM’s net-zero scenario for Japan, manymunicipalities will reach over 90% reduction of CO2 emissions, butpathways and contributions by actions differ area by area.
Example of results derived from the developed tool
Kita-kyushu city (Industrial area)

Kurashiki city (Industrial area)

Goto city (Islands area)Okayama city

92023/09/14

Monitoring of Electricity Consumptions

 NIES set up electricity monitoring system in NIES, Shinchi town inFukushima Prefecture, and Bogor (joint work with IPB team).
 We have around 90 mil. data for Shinchi town (FY2012-), 330
mil. data for Bogor, 50 mil. data for NIES (FY2009-).
Typical Configuration of Energy Monitoring System
Monitoring of energy consumption

Data collection

Data visualization

Set up in board

DataMeters(V, A, PF)

Data transfer by Wifi or 3G

Storing data incloud server

Examples of Monitoring data (case of NIES)Data visualization
system2023/09/14

Application of AI/ML: Interpolation of missing data10

 Numerical experiment is conducted for interpolation of monitoringdata by using AI/ML.
 Short-term reproducibility is relatively high, but generallysmooth results.
 Random and high power consumption factors (large facilityoperations) seem not be captured well.
Results of numerical experiment of data interpolation with NIES data
Explanatory variables: date, holiday dummy, temperature, humidity, rain falls, solar radiation, wind speed, discomfort index

Missing data: 2 daysSunMonTue

Missing data: 3 days

MAPE: 0.044

WedThu

MAPE: 0.049

MAPE: Mean Absolute Percentage Error

Missing data: 7 days

MAPE: 0.036

Missing data: 21 days

MAPE: 0.047

112023/09/14

Application of AI/ML: Forecast of future consumptions
 We constructed an LSTM*-based electricity consumption
forecasting model, using FY2020 data of NIES as a training set;
Numerical experiments is conducted to forecast electricity
consumptions in FY2021.
 Results seem to show a similar trend of actual situation.
 We will develop a methodology and try to identify effects ofenergy saving actions by using monitoring data.
Results of numerical experiment

Whole NIES

Impacts of forecasting period and RMSE****RMSE: Root Mean Squared Error*LSTM: Long Short-Term Memory, a kind of Recurrent Neural Network (RNN)