全球各地實施碳交易制度以來，已如預期達到溫室氣體減量，例如歐盟於 2010年已減排8%(相較2005年)及日本東京都2013年已減排23%(相較於基準年 (2002-2007年)的帄均排放量)，此外，也激勵不少節能與綠能投資活動，例如全 球已有88個國家註冊6,800個CDM/JI計畫，總投資金額達到2,150億美元。由此可 知碳交易制度對因應氣候變遷的重要性。然而，現行碳交易制度也面臨諸多問題 與挑戰，例如排放上限如何決定問題？碳權核配如何降低產業碳風險與碳洩漏問
805 . 0 , 168 .
0
h
805 . 0 , 168 .
0
h
29
題？如何穩定碳價波動問題？如何公允揭示碳交易會計問題？及如何防制碳交 易詐欺犯罪問題？等，均是最佳碳交易制度設計應思考問題。
本研究並以建立國家溫室氣體減量目標量化管理機制為例，參考UNFCCC 第三條建議之QELROs精神，利用最適控制模型分析方法，並納入台灣的部門節 能減排投資資料，規劃符合成本有效的國家QELROs，及國家碳預算，作為部門 排放權核配依據。
1.7 參考文獻：
台灣大學人文社會高等研究院(2013)，台灣溫室氣體減量進程與綠能 產業發展政策之基礎研究，行政院國家科學委員會專案研究計畫。
李堅明、曾瓊瑤、李叢禎 (2007)，清潔發展機制、技術創新與溫室氣 體減量策略之研究，都市與計畫，《中華民國都市計畫學會會刊》，第三 十四卷，第一期，頁39-56。
李堅明 (2013), 我國溫室氣體減量額度交易管理制度建置暨額度交易 登錄帄台維運專案工作計畫，行政院環保署專案研究計畫，財團法人環境 資源研究發展基金會。
經濟部 (2011)，「2020年及2025年部門CO2減量規劃」，行政院節能 減碳推動會簡報資料，頁6。
Bureau of Environment of Tokyo Metropolitan Government (2013), The Tokyo Cap and Trade Program Achieved 23% Reduction in 2rd year.
Bramoulle, Y. and Olson L. J. (2005). Allocation of Pollution Abatement under Learning by Doing, Journal of Public Economics, 89 (10), 1935-1960.
Center for Climate and Energy Solutions (2012), California Cap-and Trade Program Summary.
Lee Chien-Ming, Chien-Yi Yeh (2013), How to Achieve the GHG Pledge in Taiwan – An Assessment of Abatement Potential for Energy Investment, International Joint Conference on Changing Energy Law and Policy in Asia Region, National Tsing Hua University, Hsinchu, Taiwan.
EU (2013), The EU ETS is Delivering Emission Cuts.
Goulder, L. and Mathai, K. (2000).Optimal CO2 Abatement in the Presence of Induced Technological Change, Journal of Environmental Economics and
Management, 39: 1-38 .
Interpol (2013), Guide to Carbon Trading Crime, Environmental Carbon Program.
Sijm J.P.M. et.al.,(2007), Options for Post -2012 EU Burden Sharing and EU ETS Allocation, Energy Research Center of the Netherlands.
30
Simeonova K. and Gois V. (2011). Transforming Pledges into Quantified Emission Limitation or Reduction Objective (QELROs), Panama, UNFCCC
Secretarit.
UNFCCC(2010), Issues relation to the transformation of pledges for emission reductions into quantified emission limitation and reduction objectives.
World Bank (2013), Mapping Carbon Pricing Initiatives…Developments and Prospects 2013.
31
附件一： 投稿 2014 東亞環境與資源經濟學會(EAAERE)學術 研討會
How to Achieve the GHG Pledge in Taiwan – An Assessment of Abatement Potential on Energy Investment
Chien-Ming Lee, Chien-Yi Yeh
26Abstract
Taiwan‟s government has established greenhouse gas (GHG) emission reduction objectives, by 2020 as well as 2025 respectively. However, how to transfer this pledge to become quantified objectives management, confirming how this target will be achieved, is a key issue for climate policy in Taiwan. This paper expands the results of Lee and Chang (2013) to explore the significance of energy investment for the design of the Quantified Emission Limitation or Reduction Objectives (QELROs) planning, in Taiwan. In addition, this research applies optimal control methodology and empirical analysis to evaluate the potential abatement of GHG on energy investment, includes energy efficient, green energy as well as clean energy technology, among various sectors. It reveals that energy sector is the biggest GHG abatement potential sector. According to the results of GHG abatement potential on various sectors, the research plans a three phases carbon budgets, and the optimal emissions trajectories are obtained behind these carbon budget are “Inverse U ” shapes.
Keywords: QELROs, Carbon Budget, Climate Change Policy, Energy Investment.
JEL Classification: Q21, Q25, Q28
26 Corresponding author: Chien-Ming Lee, associate professor of Natural Resource Management Institute, National Taipei University, 151, University Rd., San Shia, Taipei, 23741 Taiwan, Taiwan.
Tel: (02)26748189#67335; Fax: (02)2503-9083: E-mail: cmlee@mail.nptu.edu.tw. Yeh is an assistant researcher of Taiwan Research Institute. The Corresponding author would also thank the National Science Council of Taiwan for financially supporting this research under contract NSC101-3113-P-004-011.
32
1. Introduction
The UNFCCC (United Nations Framework Convention on Climate Change, 2010) decided parties will urgently work towards a huge reduction in global greenhouse gas (GHG) emissions. This required a limitation of a global average temperature below 2 °C compared with pre-industrial levels, as well as a attaining a global peaking of global greenhouse gas emissions as soon as possible. To account for emissions and assigned amounts by parties, the UNFCCC (2012) requires parties to submit their quantified emission limitation or reduction objectives (QELROs).27The QELROs, expressed as a percentage in relation to a base year, denotes the average level of emissions that annex B parties could emit on an annual basis, during a given commitment period. In other words, pledges represent the end point of a trajectory of emissions that a party sets to achieve.
Nordhaus (1993) is a pioneering paper that discusses climate change policy. It has facilitated a great deal of research in this field. In particular, what the relationship is between greenhouse gas abatement knowledge, and optimal emission path planning.
There are two main methodologies to discuss optimal emission time path decision in literature, one is the benefit-cost criterion (such as Schlesinger and Jiang, 1990;
Nordhaus, 1993; Peck and Teisberg, 1995; Janssen, 1997; Schultz and Kasting, 1997;
Hope, 2008; Aaheim, 2010; and Anthoff, 2011), the other is cost-effectiveness criterion (such as Wigley et al., 1996; Ha-duong et al., 1996; 1996; Goulder and Schneider, 1999; Nordhous, 1996; Goulder and Mathai, 2000; Bramoulle and Olson, 2005; Lee et al., 2007). According to the literature, its topics are limited to finding out the optimal emission time path,
but not to investigate whether the GHG reduction pledge will be achieved during the commitment periods.
Under the QELROs,there has been much interest in transforming the optimal emission time path into a carbon budget that can trace the performance of a GHG reduction target achievement.
Taiwan‟s government has established greenhouse gas (GHG) emission reduction objectives, by 2020 as well as 2025 respectively. However, how to transfer this pledge to become quantified objectives management, confirming how this target will be achieved, is a key issue for climate policy in Taiwan. This paper expands the results of Lee and Chang (2013) to explores the significance of climate change policy and energy efficient investment for the design of the QELROs planning, in Taiwan. In addition, this research applies optimal control methodology and empirical analysis to evaluate the potential abatement of GHG on energy investment, includes energy efficient, green energy as well as clean energy technology, among various sectors.
This paper is organized as follows: Section 2 addresses QELROs. Section 3 lays out the analytical model for a cost-effective policy criterion. Section 4 presents and interprets the empirical results of energy investment. The final section offers conclusions, and indicates directions for future research.
27 The UNFCCC firstly provide the QELROs in “Bonn Climate Conference” in 2010.
33
Table1 Greenhouse gas emission limitation planning sectors by 2020, 2025
SECTOR EMISSION SHARE IN BASE YEAR (%)
GHG EMISSION LIMITATION BY 2020(MTCO2)
GHG EMISSION LIMITATION BY 2025(MTCO2)
Energy 8.2 20.6 17.6
Industry 49.8 125.3 107.3
Residential 13.2 33.2 28.4
Commercial 13.8 34.8 29.7
Transportation 13.7 34.5 29.7
Agriculture 1.3 3.3 2.8
Total 100.0 251.7 215.5
note1：(1)Base year emissions: average emission of 2006, 2007 and 2008 three year.
Source: Ministry of Economic and Affairs (2011).
2. QELROs
2.1 Definition of QELROs
The transformation of pledges into QELROs situates the pledges in the context of a commitment period and related accounting of emissions and removals. In practical terms, it involves calculating the average annual emissions relative to a base year that would fit the emissions trajectory leading to the pledged target.
Figure 1 Illustrates this for an Annex B party whose QELROs for the first commitment period is 95%. This party is allowed to emit, on average, an amount equivalent to 95% of its emissions in 1990 during each year of the commitment period.
GHG emissions
Figure1. QELROs and assigned amount
Source: UNFCCC (2010), Issues relating to transformation of Pledges for Emission Reductions into quantified emission limitation and Reduction Objectives.
34
2.2 Calculation of QELROs pledges
Calculation formula of the QELROs as follows (Simeonova and Gois ,2011):
QELROs mY
m c
, (1)
,
e s
e s
Y Y
E m E
(2)
c E
s mY
s, (3) Equation 1 is the calculation formula of the QELROs, whereY is the middle
m time of commitment year, for an example, when post Kyoto commitment period is 2013 to 2020, thenY equal to 2017;
mY is the year of starting point of the emissions
s trajectory, i.e.Y equal to 2013;
sY is the end year of pledge, i.e.
eY equal to
e2020;
E is the level of emissions at the starting point (2013) or average allow
semission;
E is the level of emissions at the end point (2020) or pledge emission;
e mis the slope of emission trajectory (see equation 2); cis the intersect(see equation 3).According to the equation 2, realizing the slope of emission trajectory is
relationship with abatement potential of the economy status quo. In addition, in order to define the QELRO (2013-2020) the following is required: (1) Total allowed emissions: determined by parties pledge; (2) Base year emissions: length of the commitment period.
3. The model
3.1 Behavior equation
3.1.1 Abatement equation
Assume greenhouse gas abatement function by the
i th sector at t year as
follows:
A
it A
i0e
ait, (4) WhereA is total abatement amounts (or accumulation amounts) by the
iti th
sector att year; A is total abatement amounts by the
i0i th sector at starting year(or
initial year);a is abatement factor,
it 28reflecting abatement rate by thei th sector at
t year, therefore, a is a control variable by sector.
it3.1.2 Knowledge accumulation function
28 Abatement rate means change rate of abatement in each year, indicating that can accurate to achieve commitment at pledge year.
35
This research considers abatement activities or efforts will result in accumulate abatement knowledge (i.e. learning by doing effect). In other words, the abatement amount has been achieved. This has let to an accumulation of different abatement knowledge, resulting in various abatement abilities, in the future. This affects their optimal emissions time path planning or QELROs. Assuming the knowledge
accumulation equation (or state equation) as follows: (see Goulder and Mathai, 2000;
Lee et al., 2007) Where
H is the stock of knowledge characteristic technology at time t ;
itH is
it the stock of abatement knowledge accumulation;
is the stock of abatement knowledge accumulation function. Assuming
()has the followingproperties:
H / H 0
,
HH
2
/ H
2
0,
a
/ a
0,and
aa
2
/ a
2
0.
ipresence a contribution rate of knowledge stock for knowledge accumulation ( or a call for a stock effect). This reflects abatement activities in the past, helping knowledge stock accumulation at timet , assuming 0
≤
i≤1;h represents a contribution rate both knowledge stock accumulation and
i abatement amount accumulation function for knowledge accumulation ( or a call for a interaction effect). This reflects abatement activities at timet to assist the
knowledge stock accumulation at time
t , assuming 0 ≤ h ≤1.
i 3.1.3 Abatement cost functionLet
C
it(a
it,H
it)be the economic abatement cost function of thei th sector at
two properties imply that increased knowledge reduces the respective, total and marginal costs of abatement.3.1.4 Business as usual emissions function
Assuming business as usual (BAU) emission equation as follows:
0 ,
3.1.5 Emissions function
According to previous research obtained, this is emissions equation for the i th sector at time
t :
29 This study uses abatement cost and abatement technology to be as a abatement potential proxy variable.
36
3.1.6 Emission reduction pledge function
The limitation of GHG emissions at the end of the pledge year is as follows:
iT,
iT
E
E
(8) WhereE is the emissions limitation of the i th sector at time T year (pledge
iT year). Assuming there areI sectors within a country, then the total emissions
limitation of the country must satisfy the following equation:
IiE
iT
IiE
iT E
T,
(9) Equation (9) indicates that the sum of all sector emissions at time T (end of the pledge year) must be less than the sum of their pledge emissions (or emissions limitation) (E ).
T3.2 Optimal Time Path
This paper considers optimal abatement when the policy criterion is for
cost-effectiveness. To achieve a minimum cost target GHG emission constraint, by a specific future date, see equation (9), and must be maintained. The planner‟s problem is to choose the time path for abatement (or emission) that minimizes the cost of
achieving the GHG emission reduction pledge. Formally, the optimization problem is:
The current-value Hamiltonian associated with the optimization problem for
T
t
is:30
1
liC
i( a
i, H
i) [ H
i h ( H
i, a
i)],
(11)30 This Hamiltonian actually corresponds to the problem of maximizing negative costs. This formulation is useful because it yields shadow prices with signs that match intuition.
37
For
t T
, however, the problem must form the following Lagrangian:
L
1
1
1(E
iT E
iT), (12) Where
is the co-state variable, representing the shadow price of knowledge for abatement, or the equivalent, the benefit of learning from abatement activities;
1 represents the shadow cost of GHG emissions, or the equivalent, the benefit from an incremental amount of abatement (a small reduction in the GHG emissions). From the maximum principle, we obtain a set of Kuhn-Tucker conditions:1
0 ,
By equation (13), this is equal to the marginal abatement cost (ai
C ) at the
optimal level of abatement. Equation (13) states that abatement should be pursued up until the point at which marginal cost equals marginal benefit of abatement activities ( h
ai). While equation (14) is a transversality condition, it states that the optimal solution must be satisfied by the shadow price (
1) multiplied by the gap of real emission and pledge emission) (E
iT E
iT) equal to zero. This implies that at the end of the pledge year, either
1 0
orE
iT E
iT, and thereafter.3.2.1 Solving the shadow price
From the maximum principle, we obtain a shadow price equation as follows:
,
Equation (15) states that the optimal shadow price of abatement knowledge stock grows at the rate (
r h
H) (at least for points in time up until T).Rewriting equation (15), obtaining the differential equation of shadow price for abatement knowledge stock (
):, ) ( r h
H C
H
(16) Solving the equation (16) (detail solution process, please see appendix 1), we can obtain shadow price, i.e.
(1 e
c(tT))C
H /c
. Then substitutes
into equation (13), Resulting as follows:
C
a ( 1 e
c(tT)) h
aC
H/ c 0 ,
i
i (17) 3.2.2Solving the knowledge stock
38
For the state equation of the knowledge stock (see equation (5)), the general solution of the knowledge stock (detail solution process, please see appendix 2):
,
3.2.3Solving the abatement factor
To simplify the solution, assuming the abatement cost function presence
C
, and knowledge accumulation function presence
(a
,H
) a
1 H
.31Therefore HHence, we can obtain a marginalabatement cost, marginal knowledge cost, marginal knowledge product of abatement (or learning), as well as a marginal knowledge product of knowledge stock,
is
C
a a
,
H 1
, respectively. Furthermore, substituting these results into equation (17) and equation (18) respectively, and obtaining the optimal abatement factor as follows: (detail solution process, please see appendix 2)1 , Using equation (19) and equation (4) get the optimal abatement time path sectors.
From equation (7), a further optimal emission time path is created:
0 , From equation (20), it‟s realized there are lots of factors to affect the optimal emission time path, including
B ,
0A , g ,
0
,h
, andr etc.
4. Empirical results
4.1 Knowledge accumulation equation on energy investment
To assess the GHG abatement potential of energy investment on various sectors, this study modifies the equation (5) as follows:
Where,
I is energy investment at time
TT ,to stand the stock of knowledge
accumulation at timeT ;
iis the stock effect of energy investment accumulation for knowledge accumulation on sectori ; h is the interaction effect both energy
i39
investment and abatement amount accumulation for knowledge accumulation on sector
i . Therefore, equation (21) is an empirical equation, where
iandh are
i estimated parameters.4.2 Explanation of empirical data
Institute for Advanced Studies in Humanities and Social Sciences (2012) surveyed fore major GHG emission sectors, such as energy, industrial, building, and transport sectors (see table 2). Table 2 provides selected sector and sub-sector with its related technologies (or samples). Institute for Advanced Studies in Humanities and Social Sciences (2012) used bottom-up methodology to estimate the GHG mitigation potential as well as its correspondence investment value on various sectors by 1015, 2020, 2025 and 2030. (more detail data please see appendix 3)
Table 2 Selected Sectors and Samples
Sector Sub-sector Samples (piece) Industry Iron & Steel
Chemical
Information & Technology
54 Energy Electricity
Oil and Natural Gas 23 Building Public service
Residential and Commercial 34 Transport Road transport 13
Total 124
Source: Institute for Advanced Studies in Humanities and Social Sciences (2012)
4.3 Empirical results
This study use ordinary least square methodology to estimate the parameters of knowledge accumulation equation on various sectors. The regression results are showed in table 3. Table 3 point out that all of statistics parameters are significant on various sectors. These imply high confidence of regression equations. Investigating all of estimated values of regression equation are less than 1, this imply the results to match the rationality of theoretical model.
Table 3 Empirical result of knowledge accumulation equation on various sectors
Sector Regression equation
R
240
Note1: number of quote is P value
Note 2: “*” means 90% confidence; “ ** “ means 95% confidence; “ *** “ means 99% confidence.
5. QELROs and Carbon Budget Planning
5.1 GHG BAU emission in Taiwan
To project the business as usual (BAU) of GHG emissions in Taiwan, this paper use the historical GHG emissions data from 1990 to 2011, 32and to calculate the period average GHG emission growth rate annually, which is approximately 4.02%.
Further, this research assumes the average growth rate annually is fixed on 4.02%
from 2012 to 2025, then obtains the BAU GHG emission from 2012-2025 in Taiwan, see table 4.
Table 4 BAU GHG emission (2012-2025) in Taiwan
Unit: 1,000toneCO2
year BAU Emissions
2012 259,047
5.2 The carbon budget planning in Taiwan
32 Refer to Bureau of Energy (2012), CO2 emission from fuel combustion statistics and analysis in Taiwan.
41
This section further take into account the GHG pledge (215.5MtCO2)(see Table 1) by 2025, and incorporate the knowledge accumulation regression coefficient of national wide regression equation, i.e.
0 . 168
andh 0 . 805
, into equation(19), obtains the optimal abatement rate, and through the equation (20), this paper can get the optimal emission trajectory or QELROs in Taiwan. Base on the QELROs, and GHG pledge by 2025, this study further obtain three phase (phase 1: 2012-2016;phase 2 : 2012-2017; and phase 3 : 2022-2025) of carbon budget respectively, in Taiwan, see table 5 and figure 2.
Table 5 Three phase carbon budget planning in Taiwan
Unit:1,000toneCO2
total 1,403,639 1,678,782 1,202,218
08 .
0 r
per year 280,734 335,853 301,057
total 1,403,668 1,679,265 1,204,226
10
.
0 r
per year 280,737 335,914 301,377
total 1,403,686 1,679,569 1,205,506
805
42
Figure 2 QELROs and three phase carbon budget planning, in Taiwan
6. Conclusion
Global GHG emissions are increasing rapidly and, in May 2013, carbon-dioxide (CO2) levels in the atmosphere exceeded 400 parts per million for the first time in several
hundred millennia (IEA, 2013). IEA (2013) also indicates that the world is not on track to meet the target agreed by governments to limit the long-term rise in the average global temperature to 2 degrees Celsius (°C). The Kyoto Protocol (Article 3) emphasizes annex I countries should calculate pursuant to their quantified emission limitation and reduction commitments. Therefore, enhancing the intensive actions to reach the GHG pledge by 2020 is the key climate change policy internationally.
This research is the pioneer study for investigating how to achieve the GHG pledge by 2025 in Taiwan. This paper has employed theoretical models and empirical study to
examine the GHG abatement potential of energy investment in Taiwan. Through the empirical result, the study points out that the energy sector is the biggest GHG abatement potential of energy investment in Taiwan.
This study also provides a three phase carbon budget under optimal QELROs planning. It may be useful for the government to investigate whether the launching of energy investment can achieve the GHG emissions pledge by 2025. This approximates the situation implied by recent policy proposals of the Taiwanese government.
805 . 0 , 168 .
0
h
CB1 CB2 CB3 BAU QELROs
43
Appendix 1
A.1 Solving the shadow price
Rewrite the equation (16), Obtaining the differential equation of the shadow price as follows:
, ) ( r h
H C
H
(A1) The general solution of the equation (A.1) is:
c
, (A2) Where
is a particular solution;
cis a complementary solution. Let
c Be
ct,then, getting the general solution of the shadow as follows:
Be
ct
(A3) 1. solving
Under steady state,
0
. The substitutes 0
into equation (A.1), then the optimal value of the shadow price is solved as follows:Under steady state,