Quantify the energy and environmental benefits of implementing energy-efficiency measures in China’s iron and steel production

Abstract

As one of the most energy-, emission- and pollution-intensive industries, iron and steel production is responsible for significant emissions of greenhouse gas (GHG) and air pollutants. Although many energy-efficiency measures have been proposed by the Chinese government to mitigate GHG emissions and to improve air quality, lacking full understanding of the costs and benefits has created barriers against implementing these measures widely. This paper sets out to advance the understanding by addressing the knowledge gap in costs, benefits, and cost-effectiveness of energy-efficiency measures in iron and steel production. Specifically, we build a new evaluation framework to quantify energy benefits and environmental benefits (i.e., CO₂ emission reduction, air-pollutants emission reduction and water savings) associated with 36 energy-efficiency measures. Results show that inclusion of benefits from CO₂ and air-pollutants emission reduction affects the cost-effectiveness of energy-efficiency measures significantly, while impacts from water-savings benefits are moderate but notable when compared to the effects by considering energy benefits alone. The new information resulted from this study should be used to augment future programs and efforts in reducing energy use and environmental impacts associated with steel production.

Introduction

China is currently facing significant challenges in energy use, and emissions of associated air pollutants and carbon emissions. Controlling emissions of air pollutants and CO₂ not only is important for protecting the environment, but also is essential for achieving sustainability in the country’s economic and societal development. According to International Energy Agency (IEA), more than one-third of global energy consumption and 36 % of CO₂ emissions are attributable to manufacturing industries (IEA, 2007). According to the 5^th Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), greenhouse gas (GHG) emissions from industry (30 % of total global GHG emissions) arise mainly from material processing. For example, production of iron and steel and nonmetallic minerals results in 44 % of all industrial CO₂ emissions (IPCC, 2014). The crude steel production in China was 731 Million tonnes (Mt) in 2012, accounting for half of the world’s total annual production (WSA, 2013). With such a high level of production and related energy consumption and CO₂ emissions, China’s iron and steel industry must play an important role in the country’s energy savings and emission reduction programs (Wang 2014b).

To improve energy efficiency and mitigate CO₂ emissions, Chinese governments have implemented many efficiency measures since the last decade. During the 11^th and the 12^th Five Year Plan (FYP), the National Development and Reform Commission (NDRC) released a series of National Extension Directories of Important Energy Conservation Technology (NDRC, 2008, 2009, 2011a, 2011b, 2012, 2013); the Ministry of Industry and Information Technology (MIIT) established the Energy Savings and Emission Reduction Information Platform and released the Guidebook of Advanced and Applicable Energy Savings and Emission Reduction Technologies in Iron and Steel Industry in 2012 (MIIT, 2012a, 2012b). In addition, NDRC, MIIT and the Ministry of Environmental Protection (MEP) jointly issued the Cleaner Production Evaluation System for the Iron and Steel Industry in 2012 (MEP, 2013). These government agencies proposed about 60 energy-saving and emission-reduction measures for the iron and steel industry collectively. Although many of the measures have been proposed, lacking full understanding of costs and benefits has created barriers against implementing these measures widely. It is important to evaluate cost effectiveness of energy-efficiency measures and select the most suitable and cost effective measures for implementation.

Literature review

Developing bottom-up energy system models and energy conservation supply curves (CSC) are two common methods for quantitative analyses of specific energy-efficiency measures. With the bottom-up modeling method, Wen et al. (2014) applied the AIM model to estimate the potentials of energy conservation and CO₂ mitigation in China’s iron and steel industry during 2010–2020. Xu et al. (2014) and Karali et al. (2014) used the ISEEM model to analyze the roles of energy-efficiency measures in achieving specific carbon reduction targets in the same industry of U.S. Chen et al. (2014) also applied the China-TIMES model to study the carbon mitigation strategies and corresponding impacts. Using the CSC method, Morrow et al. (2014) analyzed 25 energy-efficiency measures applicable to India’s iron and steel industry. Earlier, Hasanbeigi et al. (2013) assessed the costs of energy savings and emission reductions from applying energy-efficiency measures in the China’s iron and steel sector. Recently, Li and Zhu (2014) also estimated the costs of energy savings and CO₂ emission reduction in China’s iron and steel production. These papers report important works for specific energy-efficiency measures of iron and steel industry; however, knowledge gaps still exist. On one hand, detailed analyses of energy-efficiency measures promoted by the Chinese government during the 11^th and the 12^th FYP are limited in those studies; on the other hand, the majority of these studies only quantify energy-savings benefits, whereas non-energy benefits are neglected, such as carbon-emission mitigation, air-pollutants reduction, and water savings that are very important for China’s sustainable development.

From a macro-perspective, inclusion of non-energy benefits would result in reducing costs and improving cost-effectiveness of energy-efficiency measures, thus influence the assessment of cost-effective potentials (Worrell et al., 2003). There are many types of non-energy benefits, such as: (1) saved water and minimized wastes, (2) reduced GHG emissions, (3) reduced air pollutant emissions, (4) saved labor and time, (5) improved working environment (Worrell et al., 2003; Lung et al., 2005; IEA, 2012). Given the limitations, uncertainties, and challenges of quantifying non-energy benefits, this study focused on three types of environmental benefits, namely the carbon emission reduction, the air pollutants reduction, and the water-savings benefits. This paper aims to advance the understanding of costs, benefits, and cost-effectiveness of energy-efficiency measures in China’s iron and steel industry, by including both energy- and environmental benefits.

Overview of China’s iron and steel production

China’s iron and steel industry has grown rapidly in recent decades. The rapid growth has been attributed largely to increasing domestic demand; this growth is expected to continue in the coming years. During 1996–2012, China’s crude steel production increased from 107 to 731 Mt; its corresponding share of world steel production increased from 13.5 to 50.0 %. Additionally, China’s iron and steel industry made great progress in improving energy efficiency during the past decade. Average intensity of total energy use in key steel enterprises decreased from 761 kgce (kilogram of coal equivalent)/tonne steel in 2004 to 592 kgce/tonne in 2013. From 2004 to 2013, energy intensity of iron-making process decreased from 466 to 398 kgce/tonne iron; BOF (Basic oxygen furnace) process decreased from 26.6 to negative 7.7 kgce/tonne; and EAF (Electric arc furnace) process decreased from 209.9 to 60.8 kgce/tonne. Large “efficiency gap” still exists between the lowest and the highest energy intensity enterprises, as shown in Table 1 (Wang 2005, 2009, 2011, 2014a, 2014b).

Table 1 Process primary energy intensity for Chinese key steel enterprises (kgce/tonne)

Methodology

In order to quantify energy and environmental benefits of implementing energy-efficiency measures in China’s iron and steel production, and to evaluate their impacts on cost effectiveness of the measures, we use the compiled data and information from literature reviews and developed a new evaluation framework in this paper, as shown in Fig. 1.

Fig. 1

Evaluation framework for quantifying energy and environmental benefits

Data collection and basic assumption

The analysis for China’s iron and steel industry is based on both international and Chinese technologies. Many energy-efficiency measures promoted by NDRC and MIIT are used in this analysis because other studies do not provide consistent and comprehensive data on energy-savings, emission-reduction, or associated costs of different energy-efficiency measures (NDRC, 2008, 2009, 2011a, 2011b, 2012, 2013; MIIT, 2012a, 2012b).

We use 2012 as the base year because that was the latest year for which energy and environmental data have been published by China’s national statistical agencies at the time of this study. Data on total production of different products are obtained from China Iron and Steel Association (CISA, 2013) and the World Steel Association (WSA, 2013). For estimating the adoption rates and technology availability of different measures, we developed a questionnaire and sent it to several experts in the Chinese iron and steel industry. Additionally, we obtained data from two recent reports: Key Industrial Energy-efficient and Emission Reduction Technologies and Measures (MIIT, 2012b) and Roadmap Study on Achieving Technical Energy Conservation Potential in China’s Industrial Sector by 2020 (ERI, 2013).

The carbon emission factors for fuels used for calculating CO₂ emissions from energy consumption are taken from the IPCC Guidelines for National Greenhouse Gas Inventories (IPCC, 2007). The emissions factor for electricity in 2012 is assumed to be 0.77 kg CO₂/kWh (NBS, 2013a). Given most of the fossil fuels used in the China’s iron and steel industry are coal and coke, we use the weighted average CO₂ emission factor for coal and coke consumed in the iron and steel industry in 2012 as the CO₂ emission factor for fuel in this research, which is approximately 83.8 kg CO₂/GJ (NBS, 2013a). In the processes of steel production and power generation, there are usually air pollution removal facilities, such as desulfurization equipment and de-nitrification equipment. For simplicity, we estimate the air pollutant emission factors based on the emissions and energy consumption in steel and power sectors, which are approximately 5 kg/tce and 1,654 kg/GWh for SO₂, 2 kg/tce and 2,114 kg/GWh for NO _x , and 4 kg/tce and 462 kg/GWh for PM₁₀ (NBS, 2013a, 2013b). Additionally, we assume that impacts from interactions among energy efficiency measures are minimal, i.e., measures are analyzed as if they were implemented separately. For this reason and to avoid overestimation of total cumulative energy-saving potential, we have used the lower end of energy-saving range that was available for each energy-efficiency measure. We also estimate the average water coefficients (i.e., water volumes per energy production unit) based on published water factors in fuel production, and power generation processes, as shown in Table 2.

Table 2 Summary of water coefficients in fuel production and power generation processes

The average unit price of electricity is assumed to be 120 $/MWh (SERC, 2011), while the average unit price of thermal coal for industrial use is approximately 111 $/tonne in 2012, which is used as the fuel price in this report (CCTD, 2013). To convert costs reported in RMB to US dollars, we use an average exchange rate of 6.31 RMB/US$ (CFETS, 2013).

Estimate energy savings, emission reductions, and water savings

The technical potential of fuel savings and electricity savings from energy-efficiency measure j can be calculated using Eq. (1) and Eq. (2), respectively

$$ S{F}_j={P}_i\cdot \left(100\%-{k}_j\right)\cdot T{A}_j\cdot R{F}_j,\ \mathrm{in}\ \mathrm{G}\mathrm{J} $$

(1)

$$ S{E}_j={P}_i\cdot \left(100\%-{k}_j\right)\cdot T{A}_j\cdot R{E}_j,\ \mathrm{in}\ \mathrm{k}\mathrm{W}\mathrm{h} $$

(2)

Where

SF _j  = technical potential of fuel savings from measure j (GJ);

SE _j  = technical potential of electricity savings from measure j (kWh);

P _i = production in step i (Mt);

k _j = current adoption rate of measure j (%);

TA _j = technology availability of measure j, the extent to which the remaining adoption potential of the technology in Chinese iron and steel industry;

RF _j = specific fuel savings for measure j (GJ/Mt-production i);

RE _j = specific electricity savings for measure j (kWh/Mt-production i).

The air pollutants considered in this study are SO₂, NO _x , and PM₁₀, without considering PM_2.5 because of a lack of reliable emission and cost data for PM_2.5. Reduction of pollutant emissions (CO₂, SO₂, NO _x , and PM₁₀) corresponding to measure j can be calculated using Eq. (3).

$$ R{C}_j=S{F}_j\cdot E{F}_1+S{E}_j\cdot E{F}_2,\ \mathrm{in}\ \mathrm{tonne} $$

(3)

Where

RC _j = emissions reduction corresponding to measure j, tonne;

EF ₁ = direct emission coefficient of fuels (tonne/GJ);

EF ₂ = indirect emission coefficient of electricity (tonne/kWh).

Water withdrawal is an important indicator for the iron and steel industry and power industry (NDRC 2013). In this study, we only consider energy related water savings, based on the energy savings and water coefficients, as shown in Eq. (4).

$$ W{S}_j=S{F}_j\cdot W{F}_1+S{E}_j\cdot W{F}_2 $$

(4)

Where

WS _j = total water savings due to measure j (m³);

WF ₁ = average water withdrawal coefficient for fuel production (m³/GJ);

WF ₂ = average water withdrawal coefficient for power generation (m³/kWh).

Quantify the benefits and evaluate the cost-effectiveness

To quantify the emission reduction benefits, we use the concept of an air pollutant (AP) price index, as shown in Eq. (5) (Mao et al., 2012; 2014).

$$ AP=A\cdot R{C}_{CO2}+B\cdot R{C}_{SO2}+C\cdot R{C}_{NOx}+D\cdot R{C}_{PM10},\ \mathrm{in}\ \$ $$

(5)

Where

RC_C02, RC_SO2, RC_NOx, and RC_PM10 represents the emission reduction of CO₂, SO₂, NO _x , and PM₁₀, respectively, in tonne.

A, B, C, and D is the price weighting factor of CO₂, SO₂, NO _x , and PM₁₀, respectively, in $/tonne.

A is the average carbon price, based on historical trading volumes and trading turnovers in China’s seven regional carbon markets, i.e., Beijing, Shanghai, Tianjin, Shenzhen, Guangdong, Hubei, and Chongqing (WHECA, 2014). As there are no trading markets for air pollutants in China, we use external environmental damage cost as the weighting factors of SO₂, NO _x , and PM₁₀ (Zhang et al., 2007; Mao et al., 2012). The price weighting factors are listed in Table 3.

Table 3 Price weighting factors for CO₂ and air pollutant emissions in the iron and steel industry

In this study, we define the carbon abatement cost for a specific measure as the change of total costs divided by the CO₂ reduction potentials, as shown in Eq. (6).

$$ {c}_j=\frac{P_i\cdot \left(1-{k}_j\right)\cdot T{A}_j\cdot \left[\frac{I_j\cdot r}{\left(1-{\left(1+r\right)}^{-n}\right)}+\varDelta O{M}_j\right]-{B}_j}{R{C}_{CO2,j}} $$

(6)

Where

c _j = carbon abatement cost for an energy-efficiency measure j ($/tonne);

P _i = production in step i (Mt);

k _j = current adoption rate of measure j (%);

TA _j = technology availability of measure j, the extent to which the remaining adoption potential of the technology in Chinese iron and steel industry;

I _j = change in total capital investment for an energy-efficiency measure j ($/tonne);

ΔOM _j = change in non-energy annual operation and maintenance cost for measure j ($/tonne);

r = discount rate (15 %), it should be noted that the choice of the discount rate depends on the purpose and approach of the analysis (prescriptive versus descriptive) used (Hasanbeigi et al., 2013).

n = lifetime (years);

B _j = benefits of measure j ($), include energy benefits (i.e., reduced energy costs) and environmental benefits (i.e., emission reduction benefits and reduced water costs);

RC _{CO2, j}  = annual reduction of CO₂ emissions for measure j.

The cost-effectiveness for specific measure is determined by the carbon abatement cost: A negative cost c _j means the measure j is cost-effective.

If we consider the change in measure cost and energy-savings benefits only, Eq. (6) becomes the following

$$ {c}_1=\frac{P\cdot \left(1-k\right)\cdot TA\cdot \left[\frac{I\cdot r}{\left(1-{\left(1+r\right)}^{-n}\right)}+\varDelta OM\right]-{p}_1\cdot SF-{p}_2\cdot SE}{R{C}_{CO2}} $$

(7)

If we consider the change in measure cost, energy-savings benefits, and carbon-reduction benefits, Eq. (6) becomes the following:

$$ {c}_2=\frac{P\cdot \left(1-k\right)\cdot TA\cdot \left[\frac{I\cdot r}{\left(1-{\left(1+r\right)}^{-n}\right)}+\varDelta OM\right]-{p}_1\cdot SF-{p}_2\cdot SE-AP}{R{C}_{CO2}} $$

(8)

If we consider the change in measure cost, energy-savings benefits, and environmental benefits (CO₂ and air pollutant emission reduction benefits and water savings), Eq. (6) becomes the following:

$$ {c}_3=\frac{P\cdot \left(1-k\right)\cdot TA\cdot \left[\frac{I\cdot r}{\left(1-{\left(1+r\right)}^{-n}\right)}+\varDelta OM\right]-{p}_1\cdot SF-{p}_2\cdot SE-AP-{p}_3\cdot WS}{R{C}_{CO2}} $$

(9)

Where

P ₁ = average fuel price ($/GJ);

P ₂ = average electricity price ($/kWh);

AP denotes the total emission reduction benefits; see Eq. (5);

P ₃ = average water price ($/m³);

WS = total water savings (m³).

Typical energy-savings and emission-reduction measures

Considering data uncertainties and information availability for some measures (e.g., emerging measures), we selected 36 energy-efficiency measures for the analyses and presentations in this study. Table 4 presents the compiled results from these measures, including energy savings, capital and change in O&M costs, adoption rates in 2012, and product amount for each process in China.

Table 4 Compiled data of 36 energy-conservation and emission-reduction measures

Results and discussion

Technical energy savings and environmental impacts

Table 5 summarizes the technical potential of energy savings, emission reduction, and water savings for each energy-efficiency measure in China’s iron and steel production.

Table 5 Technical potentials of energy savings, emission reduction, and water savings in China’s iron and steel industry (2012)

For individual measure, the regenerative burner measure (measure #28) exhibits the largest technical potential in energy savings (24,734 GWh electricity), emission reductions (19.1 Mt CO₂, 40.9 kiloton (kt) SO₂, 52.3 kt NO _x, and 11.4 kt PM₁₀), and water savings (559.7 million m³); the recovery of BFG gas (measure #13) has the lowest technical potential in energy savings (37.7 ktce fuel), emission reductions (0.1 Mt CO₂, 0.2 kt SO₂, 0.1 kt NO _x , and 0.1 kt PM₁₀), and water savings (0.1 million m³).

27 energy-saving measures are process technologies and the other nine measures are technologies for waste energy recovery. The 27 measures account for 82.8 % of fuel savings and 64.4 % of electricity savings, while the other nine measures are responsible for 17.2 % of fuel savings and 35.6 % of electricity savings. The largest potentials for energy savings and emission reduction come from the iron-making processes (27.0 %) and hot-rolling processes (25.0 %); the pellet process has the lowest potential for emission reduction (about 1.0 %).

For the 36 energy-efficiency measures, technical potential of total energy savings are about 36.5 Mtce fuels and 78,659 GWh electricity, corresponding to 7.8 % fuel savings and 15.1 % electricity savings, respectively. Annual emission reduction is 150.4 Mt CO₂, 321.8 kt SO₂, 243.7 kt NO _x, and 180.0 kt PM_10, corresponding to 10.0 %, 13.4 %, 25.0 %, and 10.0 % of the total emissions (by type), respectively; Water savings are 1,842.2 million m³ (51.5 % of annual consumption in the sector).

CO₂ abatement cost analyses

Based on the main technical cost data (change in capital and O&M costs), and associated benefits (energy savings, emission reduction, and water savings), CO₂ abatement costs are calculated for each energy-efficiency measure listed in Table 6.

Table 6 Costs, benefits, and carbon abatement costs for individual energy-efficiency measures

Table 6 indicates that both energy-savings benefits and environmental benefits have important impacts on the CO₂ abatement cost. When only energy-savings benefits are considered, the abatement costs range from negative $212/tonne CO₂ (measure #22) to $312/tonne CO₂ (measure #31); when the emission reduction benefits are considered, the abatement cost is reduced further, ranging from negative $227/tonne CO₂ (measure #22) to 295 $/tonne CO₂ (measure #31); when energy and all environmental benefits (including emission reduction and water-savings) are included, the abatement costs are reduced more, ranging from negative $251/tonne CO₂ (measure #22) to 294/tonne CO₂ (measure #31) $/tonne CO₂.

The three individual technologies having the lowest reduction cost are scrap preheating (measure #22), regenerative burners (measure #28), and generation of sinter waste heat (measure #3), all with negative abatement cost. The three technologies with the highest abatement costs are heat recovery from hot-rolling (measure #31), cyclone type top combustion hot stoves (measure #15), and Grate-Kiln (measure #10), all with positive costs.

Cost-effectiveness analyses

Based on the individual measure costs, energy benefits, and environmental benefits, we can evaluate and calculate the cost-effective potentials of energy savings, emission reduction, and water savings, as shown in Table 7.

Table 7 Total costs, benefits, energy savings, emission reduction, and water savings under three different assumptions

When we consider the energy benefits only, 19 measures are cost effective, the total costs and benefits are 1,953 and 14,132 million $, and the cost-effectiveness energy savings are 19.6 Mtce for fuel and 67,249 GWh for electricity, while the emission reduction are about 100 Mt CO₂, 214 kt SO₂, 184 kt NO _x and 109 kt PM₁₀, respectively. Additionally, the total water savings are about 1,555 million m³.

When we consider the energy benefits and emission reduction benefits, 23 measures were identified cost effective, the total costs and benefits are 3,675 and 16,047 million $, and the cost-effectiveness energy savings are 29.3 Mtce for fuel and 67,249 GWh for electricity, while the emission reduction potential are about 124 Mt CO₂, 265 kt SO₂, 204 kt NO _x and 147 kt PM₁₀, respectively. Additionally, the total water savings are about 1,572 million m³.

When we consider the energy benefits and emission reduction and water-savings benefits, 26 measures were identified cost effective, the total costs and benefits are 4,494 and 16,855 million $, and the cost-effectiveness energy savings are 29.3 Mtce for fuel and 71,164 GWh for electricity, while the emission reduction potential are about 127 Mt CO₂, 272 kt SO₂, 213 kt NO _x and 149 kt PM₁₀, respectively. Additionally, the total water savings are about 1,660 million m³.

More than two-thirds of the 36 alternative measures are cost effective when energy and environmental benefits are accounted for, while many of them are not yet to be adopted widely. The main barriers to wider implementation include the following: (1) potential hidden costs associated with collecting and analyzing information, production disruptions, and inconvenience; (2) limited access to capital needed for investing in energy-efficiency measures; (3) risk aversion due to uncertain payback of energy-efficiency measure investments; (4) imperfect information about market conditions, technology characteristics and impacts of business’ own behavior; and (5) inertia, e.g., opponents to change within an organization may result in neglecting of energy-efficiency measures (Rohdin et al. 2007).

Sensitivity analysis

The emission reduction costs and the potentials of cost effective measures are influenced by the discount rates and the price factors. We perform sensitivity analyses with three levels of discount rates (10 %, 15 %, and 20 %) and three types of environmental price factors (average, low, and high), as defined in Table 8.

Table 8 The scenarios definitions of discount rates and environmental price factors

Table 9 shows that an increase in discount rate decreases cost-effectiveness of energy-efficiency measures and results in lower levels of cost effective emission reduction and water savings. A higher environmental price factor corresponds to higher energy benefits and environmental benefits, leading to larger potentials of cost effective emission reduction and water savings.

Table 9 Sensitivity analysis of the cost effective emission reduction and water savings

When the discount rate is 10 % (Scenario 2), the environmental benefits have limited effect on the cost effectiveness of energy-efficiency measures; whereas the discount rate is higher (e.g., 15 % in Scenario 2 or 20 % in Scenario 3), the environmental benefits have more significant effect on the cost effectiveness of energy-efficiency measures. When the discount rate is 15 % and the environmental price level is relatively high, the cost-effectiveness of energy-efficiency measures is more sensitive to the price factors, whether or not to include environmental benefits. When the price factor is low, such as in Scenario 4, the effects of including environmental benefits or not on CO₂ abatement cost are more evident; however, the effects on the scales of cost-effective energy savings and emission reduction are minimal.

With only energy savings benefits, cost effective emission reduction exhibits the following ranges: 90 to124 Mt CO₂, 193 to 266 kt SO₂, 175 to 206 kt NO _x , and 93 to 147 kt PM₁₀, while water savings range from 1,548 to 1,587 million m³.

With inclusion of energy savings and emission reduction benefits, cost effective emission reduction exhibits the following ranges: 100 to 129 Mt CO₂, 214 to 277 kt SO₂, 184 to 215 kt NO _x , and 109 to 153 kt PM₁₀, while cost effective water savings range from 1,555 to 1,662 million m³.

With inclusion of all environmental benefits (i.e., CO₂ emission reduction, air pollutants emission reduction benefits, and water savings benefits), cost effective emission reduction exhibits the following ranges: 100 to 130 Mt CO₂, 214 to 278 kt SO₂, 184 to 216 kt NO _x , and 109 to 153 kt PM₁₀, while cost effective water savings range from 1,555 to 1,673 million m³.

Conclusions and recommendations

In this paper, we quantify the energy and environmental benefits, and evaluate the cost-effectiveness of 36 energy-efficiency measures under different scenarios for Chinese iron and steel industry. The results show that while energy-savings benefits are the main driver in reducing the carbon abatement costs, environmental benefits also affect the cost-effectiveness of the efficiency measures significantly. Among the environmental benefits, including emission reduction benefits in calculations reduces the carbon abatement cost substantially. While the effects from including water-savings benefits are moderate under the assumptions in this study, such effects may become more influential as water price goes up. It is both important and necessary to quantify and monetize environmental benefits when evaluating the costs of energy savings and carbon abatement associated with energy-efficiency measures. Future studies may benefit from including additional non-energy benefits.

To improve energy efficiency and narrow the “efficiency gap” in iron and steel production addressed in this paper, we recommend enhancing adoption of process energy-efficiency measures and waste energy recovery technologies, especially the cost effective measures (e.g., scrap preheating, regenerative burners, and generation of sinter waste heat). We have found that more than two-thirds of the 36 efficiency measures are cost effective when energy and environmental benefits are accounted for, while many of them are yet to be adopted more widely. The main barriers to wider implementation of cost effective measures are discussed, including potential hidden, limited access to capital, risk aversion, imperfect information, etc.

Advancing the understanding of cost effectiveness provides opportunities to diffuse cost barriers against adopting efficiency measures, and may help to promote effective programs and policies to overcome the barriers, such as development of energy-efficiency information resources, technical assistance in identifying energy-efficiency measures, and financing programs for efficiency measures. The new information resulted from this study should be used to augment future programs and efforts in reducing energy use and environmental impacts associated with steel production.