Silviase/jgov_v1.4_full_ann huggingface dataset:
表5 グレンジャー (小売物価指数)

dependent variable	exclude	dcall	dlogm1	dlogloan	dlogrpi	dlogiip
dcall	0.0135	0.5554	0.0006	0.2079
dlogm1	0.5486	0.0031	0.8238	0.0009
dlogloan	0.1705	0.9454	0.7734	0.9363
dlogrpi	0.0000	0.6694	0.9112	0.7546
dlogiip	0.8116	0.2752	0.8834	0.1127

$ ^{*} $ 注： $ ^{*} $ 10% $ ^{**} $ 5% $ ^{***} $ 1%有意。

表6 グレンジャー (卸壳物価指数)

lag=3

dependent variable	exclude	dcall	dlogm1	dlogloan	dlogrwpi	dlogiip
dcall	0.0143	0.3828	0.0054	0.2370
dlogm1	0.3619	0.0008	0.0156	0.0001
dlogloan	0.2067	0.9539	0.9599	0.8866
dlogwpi	0.8843	0.4207	0.4636	0.2738
dlogiip	0.5229	0.3575	0.8842	0.0022

$ ^{*} $ 注： $ ^{*} $ 10% $ ^{**} $ 5% $ ^{***} $ 1%有意。
図7 インパルスレスポンス関数の結果（小売物価）

図8 インパルスレスポンス関数の結果（卸売物価）
図10 インパルスレスポンス関数の結果 (卸売物価) 生産→コールレート→銀行貸出→マネー→物価の場合
表11 インパルス累積値 (小売物価指数)

Accumulated Response of CALL:
Period	CALL	M1	LOAN	RPI	IIP
24	0.27534	0.02548	0.01549	-0.08096	0.07265
0.04034	0.04989	0.04486	0.06367	0.04965
Accumulated Response of M1:
Period	CALL	M1	LOAN	RPI	IIP
24	0.49533	3.01356	0.57190	0.45405	1.23957
0.47255	0.72881	0.60273	0.82376	0.64043
Accumulated Response of LOAN:
Period	CALL	M1	LOAN	RPI	IIP
24	0.17898	0.00298	1.18606	-0.16863	-0.05409
0.17353	0.26569	0.20864	0.28319	0.20393
Accumulated Response of RPI:
Period	CALL	M1	LOAN	RPI	IIP
24	0.31324	0.75894	-0.15700	2.25671	-0.08144
0.35019	0.45572	0.38574	0.56833	0.43369
Accumulated Response of IIP:
Period	CALL	M1	LOAN	RPI	IIP
24	0.03645	0.63584	0.03194	1.06797	1.53114
0.27640	0.43140	0.37110	0.46767	0.40829

Cholesky Ordering: CALL M1 LOAN R P I IIP

Standard Errors: Monte Carlo (100 repetitions)

(注)下段の数字は標準誤差

表12 インパルス累積値 (卸売物価指数)

Accumulated Response of CALL:
Period	CALL	M1	LOAN	WPI	IIP
24	0.27645	0.05464	0.02529	-0.03936	0.09153
0.04972	0.05916	0.04870	0.05382	0.06258
Accumulated Response of M1:
Period	CALL	M1	LOAN	WPI	IIP
24	0.26221	3.04798	0.64145	1.15806	1.26535
0.46104	0.72105	0.52833	0.74796	0.63362
Accumulated Response of LOAN:
Period	CALL	M1	LOAN	WPI	IIP
24	0.13574	0.02707	1.18752	-0.04894	-0.04858
0.15261	0.22407	0.18333	0.20291	0.18566
Accumulated Response of WPI:
Period	CALL	M1	LOAN	WPI	IIP
24	0.06782	1.14473	-0.02994	3.29066	-0.36132
0.47036	0.66686	0.65630	0.73495	0.57349
Accumulated Response of IIP:
Period	CALL	M1	LOAN	WPI	IIP
24	-0.10155	0.55786	0.06312	0.99975	1.58048
0.24732	0.40977	0.30680	0.42804	0.31999

Cholesky Ordering: CALL M1 LOAN WPI IIP

Standard Errors: Monte Carlo (100 repetitions)

(注)下段の数字は標準誤差
(1) 小壳物価指数

Series: M1 LOAN RPI IIP CALLLags interval: 1 to 3
Data Trend:	None	Linear	Linear
Rank or No. of CEs	InterceptNo Trend	InterceptNo Trend	InterceptTrend
Trace	1	0	1
Max-Eig	1	1	2

## (2) 卸壳物価指数

Series: M1 LOAN WPI IIP CALLLags interval: 1 to 3
Data Trend:	None	Linear	Linear
Rank or No. of CEs	InterceptNo Trend	InterceptNo Trend	InterceptTrend
Trace	1	0	1
Max-Eig	1	0	1

表 14 共和分係数 (小壳物価指数)

Data Trend: NoneRank or No. of Ces: Intercept No Trend1 Cointegrating Equation(s): Log likelihood 2653.66911
LOGM1	LOGLOAN	LOGKOURI	LOGIIP	CALL	C
1	-0.092260.18057	-0.747800.08483	-0.390290.06242	-0.529651.09359	-5.356242.88120
Data Trend: LinearRank or No. of Ces: Intercept No Trend1 Cointegrating Equation(s): Log likelihood 2655.64915
LOGM1	LOGLOAN	LOGKOURI	LOGIIP	CALL
1	-0.060710.16729	-0.718030.07859	-0.324670.05783	0.458991.01317
Data Trend: LinearRank or No. of Ces: Intercept Trend1 Cointegrating Equation(s): Log likelihood 2659.17829
LOGM1	LOGLOAN	LOGKOURI	LOGIIP	CALL	@TREND
1	-1.015860.29952	1.096130.39664	-2.130620.41300	5.827131.70647	0.018010.00401
2 Cointegrating Equation(s): Log likelihood 2676.01012
LOGM1	LOGLOAN	LOGKOURI	LOGIIP	CALL	@TREND
1	0	-0.857670.23570	-0.216500.23960	0.005111.21840	-0.001130.00229
0	1	-1.923290.25558	1.884220.25980	-5.731101.32114	-0.018840.00248

(注)下段の数字は標準誤差である。
表15 共和分係数 (卸壳物価指数)

Data Trend: NoneRank or No. of Ces: Intercept No Trend1 Cointegrating Equation(s): Log likelihood 2588.63712
LOGM1	LOGLOAN	LOGWPI	LOGIIP	CALL	C
1	-0.224280.21650	-0.650500.08760	-0.326180.06989	-1.341601.26886	-4.064213.45791
Data Trend: LinearRank or No. of Ces: Intercept Trend1 Cointegrating Equation(s): Log likelihood 2597.52339
LOGM1	LOGLOAN	LOGWPI	LOGIIP	CALL	@TREND
1	-0.707660.21382	0.462580.21038	-1.573220.28544	5.351301.41899	0.012500.00249

(注)下段の数字は標準誤差である。

表16 VECMグレンジャー（小売物価指数）

dependent variable	exclude	d(call)	d(logm1)	d(logloan)	d(logrpi)	d(logiip)
d(call)	0.0115	0.6387	0.0448	0.3298
d(logm1)	0.8342	0.0061	0.9919	0.0496
d(logloan)	0.3470	0.9531	0.6481	0.9866
d(logrpi)	0.0000	0.6534	0.9127	0.0422
d(logiip)	0.7724	0.2743	0.8758	0.3599

(注) $ ^{*} $ 10% $ \^{**} $ 5% $ \^{***} $ 1%有意。

表17 VECMグレンジャー (卸売物価指数)

dependent variable	exclude	d(call)	d(logm1)	d(logloan)	d(logrwpi)	d(logiip)
d(call)	0.0010***	0.7822	0.1749	0.5515
d(logm1)	0.8072	0.0032***	0.0040***	0.0173**
d(logloan)	0.4251	0.9176	0.8018	0.9027
d(logwpi)	0.8804	0.4180	0.4710	0.3040
d(logiip)	0.3533	0.4187	0.9087	0.0124**

(注) $ ^{*} $ 10% $ ^{**} $ 5% $ ^{***} $ 1%有意。
図18 VECMインパルス・RPI

図19 VECMインパルス・WPI
表20 VECM インパレス値 (小売物価指数)

Accumulated Response of CALL:
Period 24	CALL 0.21285	M1 - 0.00259	LOAN 0.05326	RPI - 0.12338	IIP 0.15306
Accumulated Response of M1:
Period 24	CALL 0.10143	M1 2.85510	LOAN 0.79549	RPI 0.21998	IIP 1.67251
Accumulated Response of LOAN:
Period 24	CALL 0.37663	M1 0.09350	LOAN 1.07229	RPI - 0.04695	IIP - 0.24871
Accumulated Response of RPI:
Period 24	CALL - 0.80603	M1 0.25299	LOAN 0.55003	RPI 1.45386	IIP 1.10085
Accumulated Response of IIP:
Period 24	CALL - 0.21179	M1 0.52691	LOAN 0.18503	RPI 0.92550	IIP 1.78526

Cholesky Ordering: CALL M1 LOAN RPI IIP

表21 VECM インパレス値 (卸売物価指数)

Accumulated Response of CALL:
Period 24	CALL 0.14298	M1 - 0.11370	LOAN 0.04380	WPI - 0.13674	IIP 0.17506
Accumulated Response of M1:
Period 24	CALL - 0.42527	M1 2.26692	LOAN 0.70915	WPI 0.72137	IIP 1.64317
Accumulated Response of LOAN:
Period 24	CALL 0.31274	M1 0.24330	LOAN 1.15637	WPI 0.05420	IIP - 0.13742
Accumulated Response of WPI:
Period 24	CALL - 0.44816	M1 0.53271	LOAN 0.05518	WPI 2.97409	IIP - 0.07863
Accumulated Response of IIP:
Period 24	CALL 0.03678	M1 0.74473	LOAN 0.01743	WPI 1.09880	IIP 1.49594

Cholesky Ordering: CALL M1 LOAN WPI IIP
図22 民間非金融部門の資金調達構成

(出所)藤野正三郎・寺西重郎『日本金融の数量分析』附録 戦前期金融資産負債残高表 :1871～1940年 東洋経済新報社、CDROM版。
ESRI Discussion Paper Series No.235

The merit of sectoral approaches in transitioning towards a global carbon market

by

Noriko Fujiwara with Anton Georgiev and Monica Alessi

June 2010

Economic and Social Research Institute

Cabinet Office

Tokyo, Japan
# The merit of sectoral approaches in transitioning towards a global carbon market

June 2010

Noriko Fujiwara

with contribution of Anton Georgiev and Monica Alessi

Centre for European Policy Studies (CEPS)

Brussels
## Contents

Executive summary ..... 1
1. Introduction ..... 3
2. Industry-led transnational approaches ..... 4
2.1 What drives industry towards transnational sectoral approaches? ..... 4
2.2 Coverage ..... 4
2.3 Boundary setting ..... 5
2.4 Data collection and best practices ..... 7
Data collection ..... 7
Best practices ..... 8
2.5 Potential roles of sectoral benchmarking ..... 8
3. Sectoral crediting ..... 8
4. Policy implications ..... 10
4.1 Sectoral approaches in UN negotiations ..... 10
4.2 Sectoral dimensions of EU climate policy ..... 11
4.2.1 The EU’s energy and climate package ..... 11
4.2.2 Roles of sectoral benchmarks in the EU ETS ..... 12
4.2.3 The EU’s call for a sectoral crediting mechanism ..... 13
4.3 Policies and measures in two emerging economies: Mexico and China ..... 14
4.3.1 The untapped potential in emerging economies ..... 15
4.3.2 Mexico ..... 15
4.3.3 China ..... 16
4.4 Sectoral dimensions of US climate policy ..... 17
4.4.1 Federal cap-and-trade proposals ..... 17
4.4.2 Regional Greenhouse Gas Initiative ..... 20
5. How to strengthen the effectiveness of sectoral approaches? ..... 21
5.1 Choice of performance metrics ..... 21
5.2 Reporting ..... 21
5.2.1 UNFCCC reporting requirements ..... 22
5.2.2 Progress in measurement and reporting protocols ..... 22
5.3 A compliance system for sectoral approaches ..... 24
5.3.1 Compliance system of the Kyoto Protocol ..... 24
5.3.2 A compliance system for sectoral approaches: An example of the EU ..... 25
6. Sectoral approaches as part of the post-2012 framework ..... 25
6.1 Decentralisation and flexibility in the post-2012 architecture ..... 25
6.2 How can sectoral approaches fit into the post-2012 framework? ..... 26
7. Concluding remarks ..... 26
List of Acronyms ..... 28
References ..... 30
Appendix 1. Member-state GHG emissions in non-ETS sectors for the period 2013–20 ..... 34
Appendix 2. Increase in the share of allowances to be auctioned by member states ..... 35
Appendix 3. Distribution of allowances to be auctioned by member states reflecting the early efforts of some member states ..... 36
# THE MERIT OF SECTORAL APPROACHES IN TRANSITIONING TOWARDS A GLOBAL CARBON MARKET

Noriko Fujiwara with Anton Georgiev and Monica Alessi $ ^{*} $

## Executive summary

The term ‘sectoral approaches’ means different things to different people. There are at least three main models of sectoral approaches: industry-led transnational initiatives linked to the deployment of sector-specific technologies; bottom-up developing country commitments, possibly combined with ‘no-lose’ targets; and a sectoral Clean Development Mechanism (CDM) or sectoral crediting with implications for carbon finance.

There are a number of industry-led transnational initiatives, including the Cement Sustainability Initiative (CSI) under the World Business Council for Sustainable Development as well as activities by the International Aluminium Institute and worldsteel (formerly the International Iron and Steel Institute). Each sector has developed parameters or formulae for the coverage of sub-sectors. There are also different stages involved in benchmarking: i) setting sector boundaries, ii) documenting current industry performance based on agreed metrics or key performance indicators, and iii) identifying best practices. Consideration of the complexity in setting the sector boundary and the feasibility of operation would be preconditions for allowing the future inclusion of a relevant sector in a cap-and-trade system. Datasets collected at an installation level throughout multinational corporations could be integrated in each sector. The sharing and diffusion of best practices among companies would increase operational efficiency. Similarly, the diffusion of technology within the sector could contribute to improving the performance of the least-efficient installations.

Sector no-lose targets aim at encouraging emission reductions in a given sector through a form of non-binding targets in developing countries, especially in emerging economies. A sector-crediting baseline would be established by a host country. Credits for reductions below that baseline would be issued and could be sold on the international carbon market. A sectoral crediting mechanism is seen as an instrument for scaling up financial flows to and investment in developing countries. Transition from sectoral crediting to sectoral trading is usually considered halfway on the evolution path towards a flexible mechanism, starting from the current CDM and going through the programmatic CDM, then eventually moving to a cap-and-trade scheme. These sectoral approaches are not necessarily assumed to be the best option but can be considered part of a transition towards a global carbon market. They could coexist with other policies or mechanisms.
(WBCSD), activities of the International Aluminium Institute (IAI) and those of worldsteel (formerly the International Iron and Steel Institute). The CSI initiative in the cement sector has 23 major international companies as members, which produce cement in more than 100 countries. CSI member companies and their affiliates represent over 40% of global cement manufacturing. $ ^{1} $ The IAI has 27 member companies worldwide, which are responsible for 80% of world aluminium production. $ ^{2} $ A number of non-member companies also submit annual GHG-related data. Worldsteel represents approximately 180 steel producers (including 19 of the world’s 20 largest steel companies), national and regional steel-industry associations, and steel research institutes. Worldsteel covers around 85% of the world’s steel production. $ ^{3} $

Other than industry-led initiatives, public–private partnerships could also play a role in developing transnational sectoral approaches. The APP consists of seven partner countries – Australia, Canada, China, India, Japan, the Republic of Korea and the United States. Under the APP are sectoral taskforces representing three energy-supply sectors (cleaner fossil energy, renewable energy and distributed generation, power generation and transmission) and five energy-intensive sectors (steel, aluminium, cement, coal mining, buildings and appliances).

### 2.3 Boundary setting $ ^{4} $

All the initiatives underway promote the collection of information about the status of a sector in a transparent way, for example, based on different stages in benchmarking. $ ^{5} $ In a sector-specific initiative, drawing a boundary is the first step to benchmarking and a pre-condition for data collection. Boundary setting draws a limit around what can be measured against pre-determined criteria.

1. The product of a company’s activity in a sector, especially if emissions performance is to be measured per unit of output

Standardised single-product outputs such as kWh electricity or tonne of clinker (cement sector) are easy to identify sector-wide while multiple products by each plant, such as petrochemicals and steel products, make it hard to decide on a common denominator, especially if the product mix evolves with time. It is also necessary to decide whether cement or concrete, instead of clinker, can be regarded as the final product in order to incorporate the use of other materials and off-site operations.

2. Whether and how far to go upstream and downstream in the product lifecycle and the industry value chain
(BAU) scenario. Objective data on emissions and technologies can be used to assess the performance of existing installations and to identify future potential to reduce emissions.

## Best practices

The next stage in benchmarking is identifying, sharing and diffusing best practices among companies to increase operational efficiency. The diffusion of technology within a sector could contribute to improving the performance of the least-efficient installations. In one example, upon the G8's request the IEA presented a proposal for an initiative including dissemination of good practice and capacity building, and reported progress with implementation of energy efficiency policies in G8 countries. $ ^{7} $ In another, the APP sectoral taskforces produced a set of handbooks for dissemination: a Green Handbook Peer Review prepared by the Power Generation and Power Distribution Task force (FEPC, 2007) to describe best practices for power generation; and a State-of-the-Art Clean Technology Handbook by the Steel Taskforce (Lawrence Berkeley National Laboratory and American Iron and Steel Institute, 2007) to describe best available energy-saving technologies and practices in the iron & steel sector.

### 2.5 Potential roles of sectoral benchmarking

Egenhofer and Fujiwara (2008b) identify four potential roles that sectoral benchmarking could play in the development of a global carbon market:

- If sectoral performance benchmarks are based on best practices or the best available technology in a sector, they could be used for setting a cap on emissions.

- Sectoral benchmarks could also be used for allocation, at least as long as free allocation continues.

- The linkage of carbon markets would be facilitated and accelerated by coordination in central design options such as cap-setting, (free) allocation and the monitoring, reporting and verification of emissions. Sectoral approaches, notably sectoral benchmarking, could facilitate such coordination.

- A bottom-up and sectoral approach would gather and provide more robust and more reliable data that could be used to formulate post-2012 commitments by developed countries.

These four potential roles of sectoral benchmarking have policy implications. The first three points are examined in light of the EU’s view (see section 4.2.2). The fourth point supports proposals for further commitments by developed countries and bottom-up country commitments by emerging economies (see sections 4.2-4.4).

### 3. Sectoral crediting

Sector no-lose targets (SNLTs) aim at encouraging emission reductions in a given sector through a form of non-binding targets in developing countries, especially in emerging economies. There would be no penalty for not reaching the target, hence the term ‘no lose’. A sector crediting baseline would be established by a host country, in i) intensity goals in terms of the emissions intensity of the sector as a whole, e.g. tonnes $ CO_{2} $ ( $ tCO_{2} $ ) per tonne of output or per MWh of electricity; ii) fixed emission goals in terms of an absolute total quantity of GHG emissions; or iii)
technology-based objectives (e.g. Baron et al., 2009; Schneider and Cames, 2009). Most of the existing proposals assume that the sectoral crediting baseline would be set well below the BAU emissions for the mechanism to increase financial flows and mitigation actions. Credits for reductions below that baseline would be issued and sold on the international carbon market (see Figure 1).

Figure 1. Simple depiction of a sectoral crediting baseline

Source: Ward et al. (2008).

A sectoral crediting mechanism is seen as an instrument for scaling up financial flows to and investment in developing countries. Transition from sectoral crediting to sectoral trading is usually considered halfway on the evolution path towards a flexible mechanism, starting from the current CDM and going through the programmatic CDM, then eventually moving to a cap-and-trade scheme with binding absolute reductions (e.g. Schneider and Cames, 2009; Baron et al., 2009; Aasurd et al., 2009; Fujiwara, 2009; Youngman and Diamant, 2010). These sectoral approaches are not necessarily assumed to be the best option but can be considered part of a transition towards a global carbon market. They could coexist with other policies or mechanisms.

The sector target for a host country could either be negotiated internationally at the level of the Conference of the Parties to the UNFCCC as part of a comprehensive post-2012 agreement (Ward et al., 2008) or based on benchmarks to be determined by independent experts for an industry sector (Schmidt et al., 2006). One merit of sectoral crediting is that they circumvent the need for proof of additionality, one of the main problems associated with the current CDM. An open question is whether to issue credits to companies performing below the sectoral baseline or issue them to the host government. $ ^{8} $

Unlike transnational initiatives, sectoral crediting or sectoral trading is to be implemented domestically in developing countries. Proponents of sectoral crediting and sectoral trading put
more emphasis on the largest sector for global GHG emissions, the electricity and heating sector, than those of industry-led transnational initiatives (see e.g. Amatayakul et al., 2008).

### 4. Policy implications

Based on an understanding of how the models of sectoral approaches could work, this section gives an overview of the sectoral dimensions of recent policy developments, starting with the UN negotiations, turning to the EU, two emerging economies (Mexico and China) and closing with the US, at both the federal and regional levels.

### 4.1 Sectoral approaches in UN negotiations

In the Bali Action Plan, $ ^{9} $ parties to the UNFCCC (or the ‘Convention’) agreed to launch a two-year negotiation process with a deadline for conclusion of a comprehensive agreement in Copenhagen in December 2009, setting out guidance, direction or destination. For this purpose they set up a new Ad-hoc Working Group on Long-term Cooperative Action (AWG-LCA) under the Convention.

The Working Group has discussed two types of sectoral mechanisms – sectoral crediting and sectoral trading – under Art. 1(b)(v) of the Bali Action Plan, more specifically “various approaches, including opportunities for using markets, to enhance the cost-effectiveness of, and to promote, mitigation actions”. In addition, the transnational approach was initially addressed in the context of Art. 1(b)(iv) of the Bali Action Plan, which notes the consideration of “cooperative sectoral approaches and sector-specific actions, in order to enhance implementation of Art. 4, paragraph 1(c), of the Convention”. $ {}^{10} $ Yet, it has been proposed that cooperative sectoral approaches should be associated with nationally appropriate mitigation actions (NAMAs) by developing countries (e.g. Aasrud et al., 2009) and that sectoral approaches should address emissions that cannot be attributed to any particular economy (e.g. international aviation and maritime transport). $ {}^{11} $

In Copenhagen, the parties agreed to extend the mandate of the AWG-LCA. Moreover, a large majority of parties supported the Copenhagen Accord. $ ^{12} $ Art. 1(b)(v) of the Bali Action Plan on various approaches has been incorporated into Item 7 of the Copenhagen Accord, which became operational immediately. This guidance provided in the Copenhagen Accord needs to be substantiated and translated into practical decisions in Cancún in December 2010 or thereafter.
Figure 2. Developing country emissions

Source: European Commission (2009a).

As a way of ensuring environmental integrity and improving effectiveness and governance, the EU has proposed a reform of the CDM. For advanced developing countries and in highly competitive economic sectors, the EU suggests that the project-based CDM should be phased out, in a move towards a sectoral crediting mechanism. The CDM would be kept only for LDCs. This move would also pave the way for the introduction and development of cap-and-trade schemes in developing countries that are economically more advanced. New flexible mechanisms are seen as a necessary future step in developing countries' increasing contributions to global mitigation efforts and in scaling up financial flows to these countries for their action. The European Commission suggests that the EU ETS legislation $ ^{20} $ should be used to incentivise the development of sectoral market mechanisms and to promote the CDM reform (European Commission, 2009b and 2010b).

The EU’s call for a sectoral crediting mechanism presents a set of more concrete research questions. Is deviation from the BAU achievable with host countries’ own resources or with international support? Their marginal abatement costs will possibly remain lower than those in developed countries. Is the suggested figure politically acceptable, especially to developing economies? Do they have sufficient financial resources and appropriate technologies to achieve the proposed level of emission reductions? If not, how can finance be increased while prioritising the delivery of limited resources? A related challenge is how to motivate the private sector in scaling up of finance. The last but most important question is whether sectoral crediting mechanisms would be the best instrument to address these concerns.

### 4.3 Policies and measures in two emerging economies: Mexico and China

Following the above questions about the ability of sectoral crediting mechanisms to address concerns such as financial needs, cost-efficiency and political acceptability, this section turns to