Research on global carbon abatement driven by R&D investment in the context of INDCs

Highlights

• A broad concept of energy-saving technology was introduced.

• An individual-imitating based technology transfer mechanism was adopted.

• Major emitters achieve INDCs by improving R&D investment rates to 8.8% by 2025.

• R&D investment rates need to rise to 22% of GDP by 2100 for achieving 1.5 °C target.

• Technology transfer reduces emissions effectively but cannot achieve 2 °C target.

Abstract

The intended nationally determined contributions were adopted as the national plans for addressing the climate change challenge after 2020, aiming at limiting global warming to 2 or 1.5 °C. In this context, energy-saving R&D has become an important way for reducing GHG emissions. This study used a climate-economy integrated assessment model to study the carbon reduction and climate mitigation effects of R&D investment by scenario simulation. The results show that most of the major carbon emitters cannot achieve their INDC targets by continuing their current R&D growth trends. Unless the R&D investment rates of countries increase to radically high levels, global warming by 2100 cannot be controlled to below 2 or 1.5 °C even when the major carbon emitters have approached or achieved their INDC targets. Low-carbon technology transfer will obviously reduce the carbon emissions of developing countries, but cannot achieve the 2 °C target. Considering the actual R&D capabilities of countries and the economic loss under excessive R&D input, raising R&D rates to approximately 4 or 5 percent and combining them with technology transfer and production damage measures will be a more realistic approach.

Introduction

Global climate change and mitigation have been heavily researched and remain among the most challenging issues recently being widely and intensively discussed around the world [1]. The emission of greenhouse gases by the heavy use of fossil fuels is one of the main recognized causes of global climate change, leading to global warming [2]. Since industrialization, the global concentrations of carbon dioxide, methane and nitrous oxide have significantly increased to levels far higher than those observed before industrialization. Since the foundation of the United Nations Framework Convention on Climate Change (UNFCCC), after more than 20 years of negotiations and discussion, the urgency and necessity for immediate global mitigation of greenhouse gases has been agreed to and accepted by most of the countries in the world.

In the Paris Agreement, which was adopted under the United Nations Framework Convention on Climate Change in December 2015, the intended nationally determined contributions (INDCs) were welcomed as the greenhouse gas reduction plans of countries to hold the targets of “the increase in the global average temperature to well below 2 °C above pre-industrial levels and to pursue efforts to limit the temperature increase to 1.5 °C above pre-industrial levels” in order to prevent dangerous interference with climate system and ensure economic development [[3], [4], [5]]. To date, more than 190 countries/regions, which emitted more than 95 percent of world's greenhouse gases in 2012, have submitted their INDCs. That means global climate protection has finally come into the specific implementation period. Therefore, controlling and reducing global GHG emissions, especially carbon dioxide, to mitigate global climate change in the context of the INDCs will play a significant role in the future development of the global economy and society.

The main source of GHG emissions is industrial production, especially the energy sectors, using fossil fuels that generate carbon emissions [6]. Therefore, energy-saving technology is important for reducing carbon emissions [7]. Promoting the research and application of energy-saving technologies is an effective way to mitigate industrial greenhouse gas emissions. Although some scientists believe that the decline in energy intensity caused by technological innovations in the production process will lead to a decline in energy prices and consequently may cause excessive energy consumption [8], proposing increased research and development (R&D) spending to improve energy-saving technologies for reducing the demand for energy has been widely considered as an important solution to the climate change problem for a long time [[9], [10]].

The carbon mitigation effect of R&D policy has been widely discussed, both on global and on regional scales. Many of those studies are based on methods such as statistics, measurement and data analysis, especially on the regional and national level [[11], [12], [13], [14]]. Those methods focus on the existing data and the statistical relationships among the factors, and thus cannot reflect the dynamic relations among the economy, technological progress, energy use, and carbon emissions.

To study the impacts of R&D on carbon emissions and the world economy, and to address global warming in the context of the INDCs, we should use a model that reflects the interactions among economic growth, carbon emissions, and climate change under the background of global warming. At present, the integrated assessment models (IAMs) have been widely used to solve those problems. Nordhaus analyzed the impact of induced innovation on carbon intensity using R&DICE models [15]. Popp studied the impact of R&D policies with or without other climate policies such as a carbon tax by using a modified version of the DICE model called ENTICE [9,16]. Bosetti et al. used the WITCH model to obtain optimal energy investment and R&D strategies to stabilize atmospheric greenhouse gas concentrations [17]. Nemet and Baker combined an expert elicitation and a bottom-up model to study the mitigation effects of R&D and compare it with demand side support [18]. Bosetti et al. assessed the impact of the future cost of key low-carbon technologies by comparing the outcomes from GCAM, MARKAL_US, and WITCH [19]. Marangoni et al. employed WITCH to solve the optimal R&D portfolio in four key clean energy technologies [20]. Although having achieved many significant achievements, those IAMs still have some shortcomings in their depictions of technological progress.

Traditionally, the concept of energy-saving or low-carbon technology refers to those directly reducing the demands of energy products or carbon emissions during production. In this perspective, R&D for energy-saving technologies (energy R&D) is distinct from the other forms of R&D [9]. Only R&D for reducing energy costs or for improving carbon-free energy technology reduces the carbon emissions in the production process. However, from a broad perspective, especially from the perspective of input-output analysis, the energy-saving technology should include the technology that reduces the demand for non-energy intermediate products per output, because the production of intermediate input also consumes energy, and the gross carbon intensity decreases along with the decline of intermediate demand, no matter whether it is carbon energy or not. The direct energy-saving technology is contained in this broad concept. We called this intermediate input-saving technology improvement “process technological progress”. To date, except for some CGE models, few IAMs include the broad concept of energy-saving technology. The current IAMs, especially those bottom-up models usually including detailed specified energy systems, widely adopted and implemented the learning-by-doing or learning curve approaches in which the costs of various technologies or energy/carbon intensities decrease with experience or cumulative investments [16,[21], [22], [23]], and cannot reflect the broad energy-saving technology.

Further, this situation indicates another major shortcoming of the current IAMs: an over-simplified macro-economic module that cannot depict the input-output relations among sectors. Despite those bottom-up models that typically did not include detailed modelling of the overall macro-economy, many up-down IAMs also lacked detailed input-output structure in their economic modules. Among them, the well-known DICE simplified the world economy as a whole [24]. RICE and MERGE divided the global economy into country levels, but did not contain industrial structures and ignored the economic relations among countries [[25], [26]]. The same problems existed in many extended or modified versions of DICE and RICE, such as DICE-2007, RICE-2010, and DSICE [[27], [28], [29]]. MRICES, an advanced version of RICE, designed an international economic interacting mechanism using a GDP-spillover model, but this model still cannot accurately depict the national economic relations in the context of global integration [30]. Compared with its former versions, the macro-economic structure of REMIND-v1.6 used a nested production function and contained different kinds of energies and their trade mechanisms. However, except energy, the other goods were composited as one product [23,31].

For overcoming the flaws of the models mentioned above, we adopted a new climate-economy IAM, named the Capital, Industrial Evolution and Climate change Integrated Assessment model (CIECIA for short) [32] to study the mitigation effects of R&D policies in the context of INDCs. The economic core of CIECIA is a multi-national-sectoral general equilibrium model that is modified and extended on the basis of Jin [33]. It contains a detailed national input-output structure. Based on the economic model, the broad concept of energy R&D and low-carbon technology is realized by employing a stochastic technological progress mechanism from Lorentz and Savona that is driven by knowledge stocks [9,[34], [35]]. This mechanism models each sector in the countries as a firm with intermediate input coefficients. The intermediate input coefficient is stochastically shocked in each step. The sectors accept a new set of intermediate input coefficients if its unitary cost is lower than the former. This mechanism reflects the self-selection and adoption mechanism of process technology for specific sectors. Based on this IAM, global warming, economic growth, and carbon emissions of countries between 2007 and 2100 in different R&D scenarios compared with those in the baseline are simulated. The feasibility and effectiveness of R&D policies for the INDCs and the 2 and 1.5 °C global warming control targets are also discussed. In addition, the climate-economy effects of technology transfer and diffusion are studied by adopting an individual-imitation based technology diffusion mode in this IAM.