Impacts of rising temperatures on energy demands in local industries:
A case in the Saitama Prefecture of Japan
Keita Honjo & Masayuki HaraResearcherCenter for Environmental Science in Saitama (CESS)
Global Warming Countermeasures GroupAbstract
We statistically estimated the impacts of rising temperatures on industrial and commercial energy demands in the Saitama Prefecture ofJapan. We formulated a nonlinear relationship between temperature and energy demand using a machine-learning technique. From thesensitivity analysis based on statistically-downscaled climate scenarios, we found that the temperature rise between 2010 and 2050 will
increase the total electricity demand by 8.08 PJ (10.44 PJ) under RCP2.6 (RCP8.5), while it will decrease the total fuel demand by 0.15 PJ(0.59 PJ). Electricity demand will increase in 12 industries (e.g., other, machine, and food products manufacturing), and fuel demand will
decrease in 8 industries (e.g., cement, metal, and chemical products manufacturing). Energy demand in the machine products
manufacturing industry is sensitive to the temperature rise and will increase by 2.94 PJ (3.69 PJ) under RCP2.6 (RCP8.5). The estimatedimpacts have large variances because the predicted temperature rises are different depending on climate scenarios.

•••

1. Background

3. Data

The Saitama Prefecture of Japan suffers from extremely hot summers. The
annual average temperature in the Kumagaya city has increased at a rate of
2.1°C per 100 years between 1897 and 2017.
A temperature rise caused by climate change will influence economic
activities of firms through changes in energy demand.
In this study, we statistically estimated the impacts of rising temperatures onenergy demands in 18 industries of the Saitama Prefecture.

Data period: 1990—2012
Prediction period: 2026—2050
Region: Saitama Prefecture, Japan
Number of industries: 18Climate scenarios: Statistically-downscaled scenarios developed by Dr. Koji
Dairaku (University of Tsukuba). We used temperature data computed under
the combinations of five GCMs and two RCPs (RCP2.6 and RCP8.5).
Other data sources: R-JIP Database 2017 (RIETI), EDMC Databank (IEEJ), energy
demand data of METI, and weather data of JMA.

2. Model••••Box 1: Elastic net• The elastic net estimates parameters by solving the following problem:

We assumed a linear regression model which predicts energy demand byindustry from economic and temperature indices (Figure 1).
The candidates for explanatory variables are economic production, energy
price, cooling and heating degree-days with various base temperatures, anda dummy variable for the 2011 Tohoku earthquake.
We cannot use OLS because the number of unknown parameters exceeds
the data size (N = 23, see the Data section) and the candidates for
explanatory variables are correlated with each other.
Instead, we used the elastic-net algorithm (Box 1; Zou & Hastie 2005) toremove unnecessary explanatory variables from the model.

𝐲𝐗𝛽•Electricity price

Cooling degree-days

𝜆>0𝛼 ∈ [0,1]Regularization parameter
Adjustment parameter

Unlike OLS, the elastic net is also applicable to a linear regression model inwhich the number of unknown parameters exceeds a given data size and theexplanatory variables are correlated with each other.

4. Results

Economic production

Fuel price

Data vector of a response variable
Data matrix of explanatory variables
Regression coefficient vector

Elastic net(variableselection)

Electricity demand
Fuel demand

Heating degree-days•••

Earthquake dummy
Figure 1: Structure of the energy demand model.

A temperature rise under climate change increases the total electricity
demand and slightly decreases the total fuel demand (Figure 2).
The machine products manufacturing industry (MAC) is vulnerable to thetemperature rise in the sense that both electricity and fuel demands
increase (Figure 3).
The energy demand changes estimated by the sensitivity analysis have large
variances because the predicted temperature rises are different depending
on GCMs (Figure 3).

Figure 3: Impacts of risingtemperatures on electricity and fueldemands in 18 industries. The
average levels in the 2040s arecompared with the current levels.

Figure 2: Impacts of rising temperatures on electricity and fuel demandsin the whole group of 18 industries, 2026—2050. Changes from thecurrent levels are shown.

Acknowledgement
This study was supported by the Environment Research and Technology Development Fund
2-1805 of the Environmental Restoration and Conservation Agency of Japan.

Abbreviation

Industry

AGR

Agriculture, forestry, and fishery

MIN

MiningCONConstruction

FOD

Food products

TEX

Textile products

PLP

Pulp and paper products

CHM

Chemical products

CEM

Cement and ceramic products

METMetal products

MAC

Machine products

OTH

Other products

ENG

Electricity, gas, and water supply

SLE

Wholesale and retail

FIN

Finance and insurance

EST

Real estate

TRS

Transportation and telecommunication

SRV

Other private services

GOV

Government

Reference
1. Zou H and Hastie T, 2005. Regularization and variable selection via the elastic net.
Journal of the Royal Statistical Society Series B, 67, 301-320.