emissions and a 20% share of non-fossil energy
by 2030. It is therefore necessary to introduce
stronger policies and incentives to improve
energy efficiency and renewable energy uptake
and reduce carbon emissions.
2.5.1 Policy System Design and Basic
Assumptions
Carbon pricing is a widely used policy across the
globe to address the challenge of reducing carbon
emissions. It is also an important policy option
for China to help achieve the goal of peak carbon
emissions by 2030. Carbon pricing includes
carbon emissions permit trading via cap control
and a carbon tax levied by price regulation.
Theoretically, a balanced carbon market price is
equivalent to the optimal carbon tax level, which
means that carbon tax and carbon emissions
trading can deliver the same policy effect under
given conditions. Based on this assumption,
carbon emissions are mainly controlled by
introducing an endogenous (controllable) carbon
tax in the model simulations of this report.
A carbon emissions reduction policy alone provides limited incentives, especially in the short
and medium terms. A relevant subsidy (ad valorem) is an essential policy option for driving the
diffusion of new energy technologies. This study
therefore considers a non-fossil energy subsidy
as the second endogenous variable (after carbon
tax) in the model optimisation process. These
two variables allow us to analyse the impacts of
coordinated and optimised policies on China’s
intended nationally determined contributions
target for the 2015 Paris Agreement.
The optimised endogenous (controllable)
carbon tax path requires setting an exogenous
(uncontrollable) carbon emissions cap (CAP).
The CAP used in this study is set by mainly
referring to the additional carbon emissions that
could be allocated to China under the scenario of
limiting global temperature rise to below 2°C.
Raupach et al.
8 provide the carbon emission
space allocation plan for all countries and regions
based on the principles of fairness, historical
emission inertia and mixing under the 2°C scenario. China’s cumulative carbon emissions will
reach 105.55 billion tonnes by 2050 under the
representative grandfather clause. Therefore, this
study sets the estimated value as the exogenous
CAP.
In the process of model optimisation, this
study assumes that carbon tax revenues are
always sufficient to compensate for the cost of
subsidies, and that different policy mix options
are realised by adjusting the ratio between
cumulative carbon tax and subsidies throughout
the entire simulation period (2010–50). When
calculating the cumulative carbon tax and subsidies, this study follows international estimation
practice and uses a discount rate of 5%, which is
consistent with the capital depreciation rate used
in the model. The cumulative carbon tax is the
sum of the carbon tax on three fossil fuels (coal,
oil and natural gas), and the cumulative subsidy
is the sum of the subsidies for seven non-fossil
energy technologies: biofuel, nuclear, hydro,
geothermal, wind, solar and marine.
2.5.2 Analysis of INDC Target
and Policy Optimisation
The policy optimisation results mainly reflect the
impacts of optimised policy on the 2030 peak
carbon goal and the non-fossil energy development goal.
When policy is optimised around the peak
carbon and non-fossil energy development goals,
the tax revenue from carbon pricing policy is far
higher than the cost of non-fossil energy subsidies. As shown in Fig. 25, to achieve the 2030
peak carbon emissions goal, the ratio between
cumulative carbon tax and subsidies needs to be
more than 4:1. On the one hand, the policy does
not need funding, other than the carbon pricing
revenue to meet the cost of subsidies. On the
other hand, the high ratio between cumulative
carbon pricing revenue and subsidy costs is
related to the 2030 peak carbon emissions goal.
Generally, the higher the ratio between carbon
pricing policy and subsidy policy, the greater the
possibility of achieving peak carbon emissions
8
Raupach, M. R., Davis, S. J., Peters, G. P. and R.
W. Andrew, et al. Sharing a quota on cumulative carbon
emissions. Nature Climate Change, 4: pp. 873–879,
(2014).
Special Report 2: Research on China’s Energy Demand Revolution
241
by 2030. It is therefore necessary to introduce
stronger policies and incentives to improve
energy efficiency and renewable energy uptake
and reduce carbon emissions.
2.5.1 Policy System Design and Basic
Assumptions
Carbon pricing is a widely used policy across the
globe to address the challenge of reducing carbon
emissions. It is also an important policy option
for China to help achieve the goal of peak carbon
emissions by 2030. Carbon pricing includes
carbon emissions permit trading via cap control
and a carbon tax levied by price regulation.
Theoretically, a balanced carbon market price is
equivalent to the optimal carbon tax level, which
means that carbon tax and carbon emissions
trading can deliver the same policy effect under
given conditions. Based on this assumption,
carbon emissions are mainly controlled by
introducing an endogenous (controllable) carbon
tax in the model simulations of this report.
A carbon emissions reduction policy alone provides limited incentives, especially in the short
and medium terms. A relevant subsidy (ad valorem) is an essential policy option for driving the
diffusion of new energy technologies. This study
therefore considers a non-fossil energy subsidy
as the second endogenous variable (after carbon
tax) in the model optimisation process. These
two variables allow us to analyse the impacts of
coordinated and optimised policies on China’s
intended nationally determined contributions
target for the 2015 Paris Agreement.
The optimised endogenous (controllable)
carbon tax path requires setting an exogenous
(uncontrollable) carbon emissions cap (CAP).
The CAP used in this study is set by mainly
referring to the additional carbon emissions that
could be allocated to China under the scenario of
limiting global temperature rise to below 2°C.
Raupach et al.
8 provide the carbon emission
space allocation plan for all countries and regions
based on the principles of fairness, historical
emission inertia and mixing under the 2°C scenario. China’s cumulative carbon emissions will
reach 105.55 billion tonnes by 2050 under the
representative grandfather clause. Therefore, this
study sets the estimated value as the exogenous
CAP.
In the process of model optimisation, this
study assumes that carbon tax revenues are
always sufficient to compensate for the cost of
subsidies, and that different policy mix options
are realised by adjusting the ratio between
cumulative carbon tax and subsidies throughout
the entire simulation period (2010–50). When
calculating the cumulative carbon tax and subsidies, this study follows international estimation
practice and uses a discount rate of 5%, which is
consistent with the capital depreciation rate used
in the model. The cumulative carbon tax is the
sum of the carbon tax on three fossil fuels (coal,
oil and natural gas), and the cumulative subsidy
is the sum of the subsidies for seven non-fossil
energy technologies: biofuel, nuclear, hydro,
geothermal, wind, solar and marine.
2.5.2 Analysis of INDC Target
and Policy Optimisation
The policy optimisation results mainly reflect the
impacts of optimised policy on the 2030 peak
carbon goal and the non-fossil energy development goal.
When policy is optimised around the peak
carbon and non-fossil energy development goals,
the tax revenue from carbon pricing policy is far
higher than the cost of non-fossil energy subsidies. As shown in Fig. 25, to achieve the 2030
peak carbon emissions goal, the ratio between
cumulative carbon tax and subsidies needs to be
more than 4:1. On the one hand, the policy does
not need funding, other than the carbon pricing
revenue to meet the cost of subsidies. On the
other hand, the high ratio between cumulative
carbon pricing revenue and subsidy costs is
related to the 2030 peak carbon emissions goal.
Generally, the higher the ratio between carbon
pricing policy and subsidy policy, the greater the
possibility of achieving peak carbon emissions
8
Raupach, M. R., Davis, S. J., Peters, G. P. and R.
W. Andrew, et al. Sharing a quota on cumulative carbon
emissions. Nature Climate Change, 4: pp. 873–879,
(2014).
Special Report 2: Research on China’s Energy Demand Revolution
241