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Global carbon intensity of crude oil production



Producing, transporting, and refining crude oil into fuels such as gasoline and diesel accounts for ∼15 to 40% of the “well-to-wheels” life-cycle greenhouse gas (GHG) emissions of transport fuels (1). Reducing emissions from petroleum production is of particular importance, as current transport fleets are almost entirely dependent on liquid petroleum products, and many uses of petroleum have limited prospects for near-term substitution (e.g., air travel). Better understanding of crude oil GHG emissions can help to quantify the benefits of alternative fuels and identify the most cost-effective opportunities for oil-sector emissions reductions (2). Yet, while regulations are beginning to address petroleum sector GHG emissions (3–5), and private investors are beginning to consider climate-related risk in oil investments (6), such efforts have generally struggled with methodological and data challenges. First, no single method exists for measuring the carbon intensity (CI) of oils. Second, there is a lack of comprehensive geographically rich datasets that would allow evaluation and monitoring of life-cycle emissions from oils. We have previously worked to address the first challenge by developing open-source oil-sector CI modeling tools [OPGEE (7, 8), supplementary materials (SM) 1.1]. Here, we address the second challenge by using these tools to model well-to-refinery CI of all major active oil fields globally—and to identify major drivers of these emissions.
31 AUGUST 2018 • VOL 361 ISSUE 6405 851SCIENCE
Producing, transporting, and refining
crude oil into fuels such as gasoline
and diesel accounts for ~15 to 40% of
the “well-to-wheels” life-cycle green-
house gas (GHG) emissions of trans-
port fuels (1). Reducing emissions
from petroleum production is of particular
importance, as current transport fleets are
almost entirely dependent on liquid petro-
leum products, and many uses of petroleum
have limited prospects for near-term substi-
tution (e.g., air travel). Better understand-
ing of crude oil GHG emissions can help to
quantify the benefits of alternative fuels and
identify the most cost-effective opportunities
for oil-sector emissions reductions (2). Yet,
while regulations are beginning to address
petroleum sector GHG emissions (35), and
private investors are beginning to consider
climate-related risk in oil investments (6),
such efforts have generally struggled with
methodological and data challenges. First,
no single method exists for measur ing the
carbon intensity (CI) of oils. Second, there is
a lack of comprehensive geographically rich
datasets that would allow evaluation and
monitoring of life-cycle emissions from oils.
We have previously worked to address the
first challenge by developing open-source oil-
sector CI modeling tools [OPGEE (7, 8), sup-
plementary materials (SM) 1.1]. In this Policy
Forum, we address the second challenge by
using these tools to model well-to-refinery CI
of all major active oil fields globally—and to
identify major drivers of these emissions.
We estimate emissions in 2015 from 8966
on-stream oil fields in 90 countries (SM
1.4.4). These oil fields represent ~98% of
2015 global crude oil and condensate pro-
duction. This analysis includes all major
resource classes (e.g., onshore/offshore and
conventional/unconventional) and accounts
for GHG emissions from exploration, drilling
and development, production and extraction,
surface processing, and transport to the re-
finery inlet (collectively called “upstream”
hereafter). These results are based on data
from nearly 800 references, including gov-
ernment sources, scientific literature, and
public technical reports (SM 1.4.1, 1.4.4, and
table S17). Proprietary databases are used
to supplement these data when information
is unavailable in the public domain (gener-
ally for small oil fields). The latest Intergov-
ernmental Panel on Climate Change (IPCC)
100-year global warming potential (AR5/
GWP100) factors are used in this work (SM
Figure 1 presents the first upstream country-
level volume-weighted-average CI estimates
and their corresponding error bars (see fig.
S22 for the global upstream CI map). Error
bars are computed by using probabilistic
uncertainty analysis solely associated with
missing input data (SM 1.7 and 2.4). The CI
estimates of some countries with poor data
quality (e.g., Russia) in Fig. 1 are more uncer-
tain (SM 1.4.6 and 2.3).
The global volume-weighted-average up-
stream CI estimate—shown by the vertical
dashed line in Fig. 1—is 10.3 g CO2equivalents
(CO2eq.)/megajoule (MJ) crude oil (+6.7 and
–1.7), with country-level intensities rang-
ing from 3.3 (Denmark) to 20.3 (Algeria) g
CO2eq./MJ. Carbon dioxide and methane
contribute on average 65% and 34% of total
CO2eq. emissions, respectively (SM 2.2). The
total petroleum well-to-refinery GHG emis-
sions in 2015 are estimated to be ~1.7 Gt
CO2eq., ~5% of total 2015 global fuel combus-
tion GHG emissions. This estimate of total
emissions is ~42% higher than an industry-
wide scaling of an estimate for 2015 from the
International Association of Oil and Gas Pro-
ducers (based on datasets comprising 28% of
global production with uneven geographical
coverage). See SM 3 for exploration of the dif-
ferences between our analyses.
Emissions in Fig. 1 can vary substantially
over time (9), but time-series data are gen-
erally missing on a global basis and so are
not explored here. In general, oil produc-
tion declines with oil field depletion but is
also accompanied by a substantial increase
in per-MJ GHG emissions due to use of en-
hanced recovery practices. Other factors (e.g.,
oil price, geopolitics) could also affect oil pro-
duction and thus the temporal CI (9).
Gas flaring (burning) practices have a
considerable influence on the CI. If not eco-
nomically salable, this gas is either flared,
reinjected, or vented (directly emitting meth-
ane). The estimated share of flaring emis-
sions in the global volume-weighted-average
upstream CI is 22% (i.e., 2.3 g CO2eq./MJ).
Flaring data are not widely reported by gov-
ernments or companies, so for most regions,
our analysis relies on satellite-estimated vol-
umes computed using nighttime radiometry
(SM 1.2.4 and Some important con-
ventional crude oil producers with above-av-
erage global CI, such as Algeria, Iraq, Nigeria,
Iran, and the United States, are also among
the top 10 countries in flaring observed via
satellite. The contributions of routine flar-
ing to the total volume-weighted-average CI
of these countries are estimated herein to be
~41, 40, 36, 21, and 18%, respectively. Vari-
ability between flaring data sources results
in greater uncertainty for countries with high
contribution of flaring to their CI. Figure
S27 shows that gas venting instead of flar-
ing increases the estimated GHG emissions
substantially (SM 1.2.4 and 2.6). However,
currently there is no reliable remote-sensing
technology for measuring gas venting.
As the major global producers of uncon-
ventional heavy oils, Venezuela and Canada
have high country-level CI. This is due to
energy- and CO2-intensive heavy oil extrac-
tion and upgrading. Enhanced oil recovery
by steam flooding contributes to high CI in
other locations, such as Indonesia, Oman,
and California (USA).
Although some giant North Sea offshore
fields have shown rapidly increasing per-bbl
(barrel) emissions due to depletion (9), they
have low upstream GHG intensities when
compared to many other global oil fields. This
is in part due to stringent regulations on gas
processing and handling systems and renew-
able electric-power-from-shore initiatives.
Saudi Arabia is the largest global oil producer
but has a small number of extremely large
and productive reservoirs. The country has
low per-barrel gas flaring rates and low wa-
Global carbon intensity of
crude oil production
New data enable targeted policy to lessen GHG emissions
By Mohammad S. Masnadi, Hassan M. El-Houjeiri, Dominik Schunack, Yunpo Li, Jacob
G. Englander, Alhassan Badahdah, Jean-Christophe Monfort, James E. Anderson,
Timothy J. Wallington, Joule A. Bergerson, Deborah Gordon, Jonathan Koomey, Steven
Przesmitzki, Inês L. Azevedo, Xiaotao T. Bi, James E. Duffy, Garvin A. Heath, Gregory
A. Keoleian, Christophe McGlade, D. Nathan Meehan, Sonia Yeh, Fengqi You, Michael
Wang, Adam R. Brandt
See supplementary materials for author affiliations.
852 31 AUGUST 2018 • VOL 361 ISSUE 6405
ter production—resulting in less mass lifted
per unit of oil produced and less energy used
for fluid separation, handling, treatment, and
reinjection—and thus contributing to low CI.
Figure 2 shows a global field-level CI curve
for our 8966 fields (sorted cumulatively).
This illustrates the CI heterogeneity of global
crudes (SM fig. S19 and Results Data Excel
file). Fields in the highest 5th percentile emit
more than twice as much as the median field.
Upstream mitigation measures should focus
on fields in the upper end of the CI curve.
Although crude density (requiring thermal
extraction methods) and flaring are key de-
terminants of a high CI (SM 1.5), the second
figure shows that flaring is the more preva-
lent driver: For the highest CI quartile (i.e.,
>1.2 g CO2eq./MJ) in this figure, 51% of crude
volume comes from high flare fields (yellow,
red), while 18% comes from heavy oil fields
(yellow, blue). Only 4 and 9% of crude vol-
umes from the rest of the sample (i.e., ≤11.2 g
CO2eq./MJ) come from high flare and heavy
oil fields, respectively.
The cumulative CI curve uncertainty due
to missing input data is computed via a
Monte Carlo simulation and presented in fig.
S25 (SM 1.7 and 2.4).
Although oil alternatives like electric vehicles
are rapidly growing, society is likely to use
large volumes of oil in the coming decades
(10); thus, mitigation of crude oil CI is key.
Our tools and dataset allow for improved
analysis of the benefits of emissions mitiga-
tion policies. We highlight three broad strat-
egies to reduce GHG impacts: (i) resource
management, (ii) resource prioritization, and
(iii) innovative technologies.
Performance-oriented fuel quality standard
programs based on life-cycle analysis models
have been implemented successfully and have
created new regional market drivers (e.g. in
California, British Columbia, the European
Union). Relying on market forces and credit/
debit mechanisms, these fuel-agnostic policies
do not dictate specific technologies to reduce
the emissions but rather encourage innova-
tion to comply with the quality mandates.
To achieve greater impacts, such regional
fuel standard policies are emerging nation-
ally (e.g., Canada’s Clean Fuel Standard) and,
subsequently, worldwide. These regulations
should recognize the climate impact hetero-
geneity of different crude oils (see the second
figure) to reward improved production prac-
tices with clear per-barrel incentives for the
lowest CI producers (10).
The current lack of transparency about
global oil operations makes this type of
analysis particularly challenging. Labor-
intensive data gathering (as undertaken here)
still results in large uncertainty in emissions
estimates (SM 2.3 and 2.4). Thus, it is im-
portant to adopt policies to make data from
oil and gas operations publicly available. If
done correctly, these data can be released
without affecting competitiveness of enter-
prises. Countries including Norway, Canada,
the United Kingdom, Denmark, and Nigeria
have led in this respect. As countries pledge
their commitments to reduce country-level
GHG emissions and transparent reporting
under the Paris Agreement, it is essential for
energy-intensive industries (such as the oil
and gas sector) to regularly report their an-
nual carbon footprints. New industry efforts
such as the Oil and Gas Climate Initiative are
beginning to tackle this challenge.
CI curves for four hypothetical GHG miti-
gation case studies are shown in fig. S26 (SM
1.2.2 and 2.5). Two “no routine flaring” case
studies restrict the flare-to-oil ratio (FOR) to
be no higher than the global 5th and 25 th
percentiles. A fugitive emissions reduction
scenario sets fugitive and venting emissions
to be 0.2 g CO2eq./MJ, approximately the
volume-weighted average from Norwegian
oil fields in 2015 (SM 1.2.2). Cases with no
routine flaring (moderate and extreme) have
global volume-weighted-average CI reduced
from 10.3 (current world) to 8.7 and 8.3 g
CO2eq./MJ. Achieving the fugitive and venting
reduction scenario results in 7.9 g CO2eq./MJ.
These case studies mitigate 15% [262 mega-
tons (Mt) CO2eq.], 19% (332 Mt CO2eq.), and
23% (397 Mt CO2eq.) of the current annual
global upstream estimate, respectively. A
fourth case study, including both stringent
flaring reduction and minimal fugitive and
venting emissions, reduces the global average
to 5.8 g CO2eq./MJ and results in ~43% (~743
Mt CO2eq.) annual CI reduction.
A simple calculation suggests that up-
stream emissions from oil extraction can
materially affect cumulative emissions caps.
Assume a reduction in the current global
volume-weighted-average CI to the current
25th percentile (reducing emissions by ~3 g
CO2eq./MJ). Such reductions would be pos-
sible using the mitigation case studies from
fig. S26. Given that a typical barrel of crude
oil yields ~6000 MJ, this would result in ~18
kg CO2eq./bbl emissions reduction. Also note
that IPCC scenarios—even with aggressive
adoption of alternative fuels used for trans-
port—still result in projected cumulative oil
consumption of >1 trillion barrels in the 21st
century. Thus, at least 18 metric gigatons
(Gt) CO2eq. (~12 Gt as CO2 and ~6 Gt as CH4)
could be saved over the century by mitigating
oil-sector emissions through wise resource
choices and improved gas management prac-
tices. Considering additional mitigation op-
Average CI (g CO2eq./MJ)
Trinidad & To.
Rep. of Congo
Equ. Guinea
Saudi Arabia
015105 2520 30
National volume-weighted-
average crude oil upstream GHG
intensities (2015)
The global volume-weighted CI estimate is shown by
the dashed line (~10.3 g COeq./MJ). Error bars
reect 5th to 95th percentiles of Monte Carlo
simulation to explore the uncertainty associated with
missing input data (see SM section 1.7 and 2.4).
Global volume-
weighted CI
National volume-weighted-
average crude oil upstream
GHG intensities (2015)
The global volume-weighted CI estimate is shown
by the dashed line (~10.3 g CO2eq./MJ). Error
bars reflect 5th to 95th percentiles of Monte Carlo
simulation to explore the uncertainty associated with
missing input data (see SM 1.7 and 2.4).
31 AUGUST 2018 • VOL 361 ISSUE 6405 853
portunities across the crude oil supply chain
(e.g., improved refining), 18 Gt is likely an un-
derestimate; other studies have estimated up
to 50 Gt CO2eq. reduction potential (10). For
a >66% chance to keep global average tem-
perature increases below 2°C, a total of ~800
Gt CO2 can be emitted from 2017 forward (11).
The petroleum sector reduction potentials
outlined above are material on this scale.
Extraction and processing of heavy oils
and oil sands with current technologies is
very energy- and carbon-intensive, and the
ability to reduce the intensities is challeng-
ing. Although market forces have recently
led to investment shifts based on economics
alone (12), other mechanisms exist to reduce
emissions. Solar-powered steam generators
developed for heavy oil fields in Oman and
California can provide substantial mitigation
benefit. More broadly, use of solar energy
could result in sectorwide emissions reduc-
tions on the order of 5 kg CO2eq./bbl (~1.7 g
CO2eq./MJ) (13). For some key regions with
high seasonality and poor economics of solar
technology (like Canada), using energy in-
puts with low carbon intensity (e.g., hydrogen
sourced from wind and biomass), capturing
CO2 from oil sands extraction and upgrading
facilities, and investing in new low-carbon
technologies (e.g., nanoparticle-assisted in-
situ recovery, or CO2-free production of H2
from CH4 via catalytic molten metals) would
be beneficial. In addition, low-value but high-
carbon products such as petroleum coke from
upgraded oil sands could be sequestrated in
lieu of combustion (10). Countries with di-
verse resources could reduce their national
CI by prioritizing less carbon-intensive assets
(e.g., tight oil), accompanied by stringent flar-
ing and venting management.
Flaring rates can also be reduced. The
Global Gas Flaring Reduction Partnership
(GGFR) reported a nearly continuous in-
crease in global flared gas from 2010 to 2016.
Flaring is a management and infrastructure
problem and is not an unavoidable outcome
of crude oil production. Plans for new oil
field development should incorporate con-
servation methods (i.e., capture, utilization,
and/or reinjection) to eliminate routine flar-
ing. Canadian regulations point to a method
for enforcement: For offshore fields where
flaring is excessive, production rate restric-
tions are imposed until flaring reductions
are made (14). Initiatives like the World Bank
GGFR Zero Routine Flaring by 2030 are a
start, though these could be strengthened
with international advisory, financial, and
technical aid to help countries implement
flaring reduction policies. Moreover, continu-
ous monitoring and verification are essential
not only for flare management but also for
eliminating venting and fugitive methane
emissions in the oil and gas sector. Modern
surveillance using remote-sensing technolo-
gies (e.g., flare- and methane-sensing satel-
lites) could be supported and expanded (10).
Methane fugitive emissions and venting
from oil and gas facilities are poorly detected,
measured, and monitored, and thus, can in-
crease the uncertainty associated with CI
estimates. Recently, the International Energy
Agency (IEA) estimated 76 Mt methane emis-
sions from global oil and gas operations in
2015, with ~34 Mt due to oil production (15).
This prorates to ~4.6 g CO2eq./MJ crude oil,
higher than this study’s estimate of methane
contribution (~2.6 g CO2eq./MJ averaged
from all global fields, from all fugitive emis-
sions and venting). In many cases, reducing
methane emissions can result in additional
revenues from the captured methane. IEA
estimates that around 40 to 50% of current
methane emissions could be avoided at no
net cost. The cost of mitigation is generally
lowest in developing countries in Asia, Af-
rica, and the Middle East, but in all regions,
reducing methane emissions remains a cost-
efficient way of reducing GHG emissions (15).
Important questions remain with regard to
the interactions of economics and emissions.
The CI curve in the second figure reflects
differences in CI, but crude oil production
choices are obviously influenced by the inter-
action of local production costs and the global
price of oil. A market structure without car-
bon prices neglects differences in CI shown
in the second figure. Future work needs to
examine the interaction of supply economics
and CI for different resource classes.
Data-driven CI estimates such as this work
can encourage prioritizing low-CI crude oil
sourcing, point to methods to manage crude
oil CI, and enable governments and investors
to avoid “locking in” development of high-CI
oil resources. However, future progress in
this direction will rely fundamentally on im-
proved reporting and increased transparency
about oil-sector emissions.
1. IHS CERA, Oil Sands, Greenhouse Gases, and US Oil Supply
(IHS, 2012).
2. M. S. Masnadi et al., Nat. Energy 3, 220 (2018).
3. California Environmental Protection Agency/Air Resources
Board, Low Carbon Fuel Standard (LCFS).
4. European Commission, Fuel Quality Directive (FQD).
5. Environment and Climate Change Canada, Clean Fuel
Standard (2017).
6. R. Baron, D. Fischer, Divestment and Stranded Assets in
the Low-carbon Transition [Organization for Economic
Cooperation and Development (OECD), 2015].
7. H. M. El-Houjeiri, A. R. Brandt, J. E. Duffy, Environ. Sci. 47,
5998 (2013).
8. H. M. El-Houjeiri et al., “Oil Production Greenhouse Gas
Emissions Estimator OPGEE v2.0a, User guide & technical
documentation” (2017).
9. M. S. Masnadi, A. R. Brandt, Nat. Clim. Change 7, 551 (2017).
10. A. R. Brandt, M. S. Masnadi, J. G. Englander, J. Koomey, D.
Gordon, Environ. Res. Lett. 13, 044027 (2018).
11. C. Le Quéré et al., Global carbon budget 2016. Earth Syst. Sci.
Data. 8, 605 (2016).
12. K. Gilblom, “Shell to Exit Canadian Natural Resources for
$3.3 Billion,Bloomberg, 7 May 2018.
13. J. Wang, J. O’Donnell, A. R. Brandt, Energy 118, 884 (2017).
14. C-NLOPB, Newfoundland and Labrador Offshore Area
Gas Flaring Reduction Implementation Plan. Canada-
Newfoundland Labrador Offshore Petroleum Board (2017).
15. T. Gould, C. McGlade, The environmental case for natural gas
(International Energy Agency, 2017).
The Natural Sciences and Engineering Research Council of Canada
(NSERC) provided financial support to M.S.M. Aramco Services
Company (MI, USA) and Ford Motor Company provided funding
for D.S. and A.R.B. A.R.B. also received funding from the Carnegie
Endowment for prior research and data collection. D.G. and J.K.
were funded through the Carnegie Endowment for International
Peace, with primary funding from Carnegie’s endowment and
additional funding from the Hewlett Foundation, ClimateWorks
Foundation, and Alfred P. Sloan Foundation. The authors are grate-
ful to G. Cooney from National Energy Technology Laboratory (PA,
USA) for constructive comments.
Cumulative oil production (MMbpd%)
Cumulative oil production (million barrels per day)
010 20 30 40 50 60 70 80
010 20 30 40 50 60 70 80 90
Upstream carbon intensities
g CO2
eq./MJ of crude petroleum
CI percentiles
Flare and heavy
5% 50%75%
Global eld-level upstream carbon intensity supply curve (2015)
Contribution of high aring (labeled “Flare” with FOR >75th percentile of all elds) and oil density (labeled ”Heavy” with API gravity ≤22°). Bar width reects the oil
production of a particular eld in 2015. Global GHG intensity percentiles (5%, 25%, 50%, 75%, 95%) are 4.7, 7.3, 9.1, 11.2, and 19.5 g COeq./MJ crude oil, respectively.
Global field-level upstream carbon intensity supply curve (2015)
Contribution of high flaring (labeled “Flare” with FOR >75th percentile of all fields) and oil density (labeled ”Heavy” with API gravity ≤22°). Bar width reflects the oil
production of a particular field in 2015. Global GHG intensity percentiles (5%, 25%, 50%, 75%, 95%) are 4.7, 7.3, 9.1, 11.2, and 19.5 g CO2eq./MJ crude oil, respectively.
... In addition, the carbon footprint of crude oil products shows many changes, mainly due to regional differences in crude oil quality, extraction technologies, and efficiency. According to Masandi et al. (Masnadi et al. 2018), carbon intensity reduction (CIs) has national averages ranging from about 3 to 20 g CO 2 e/MJ, around the global average of 10.3, while some single oil fields have CIs of up to 50. Jing et al. (Jing et al. 2020) reported an average nationally produced CIs from 3.3 g CO 2 e/MJ in Denmark to 29.2 g in the Democratic Republic of the Congo. ...
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Battery-electric vehicles (BEV) have emerged as a favoured technology solution to mitigate transport greenhouse gas (GHG) emissions in many non-Annex 1 countries, including India. GHG mitigation potentials of electric 4-wheelers in India depend critically on when and where they are charged: 40% reduction in the north-eastern states and more than 15% increase in the eastern/western regions today, with higher overall GHGs emitted when charged overnight and in the summer. Self-charging gasoline-electric hybrids can lead to 33% GHG reductions, though they haven’t been fully considered a mitigation option in India. Electric 2-wheelers can already enable a 20% reduction in GHG emissions given their small battery size and superior efficiency. India’s electrification plan demands up to 125GWh of annual battery capacities by 2030, nearly 10% of projected worldwide productions. India requires a phased electrification with a near-term focus on 2-wheelers and a clear trajectory to phase-out coal-power for an organised mobility transition. India’s plans to electrify transport is complicated by its reliance on coal-power. Here the authors call for diverse policy and technology solutions, including a focus on cleaner grids, electric 2-wheelers, and hybrid 4-wheelers in the near-term.
... However, there is also the tendency that the exploitation of energy resources for economic growth adversely manipulates environmental quality . For instance, the oil-producing countries contribute more to environmental degradation (CO 2 emission) due to the associated effects of the energy processing activities such as gas flaring, which contributes about 15%-40% of GHG emissions of transport fuels (Masnadi, 2018). The energy consumption of OPEC increased by 685% in 2013, while CO 2 emissions increased by 440% between 1970 and 2010 due to the burning of fossil fuels within the same period. ...
The huge endowment, exploitation and trading of carbon content energy resources by the African OPEC member countries for economic expansion substantiate the fears of increasing global warming and environmental degradation. This study explores the dynamic effects of trade flows, energy consumption and per capita income on environmental degradation in seven of Africa's OPEC member countries (Algeria, Angola, Congo, Equatorial Guinea, Gabon, Libya and Nigeria), within the framework of the environmental Kuznets curve (EKC) and the pollution haven hypothesis (PHH). By employing the bootstrap panel cointegration test and the PMG/ARDL estimation technique on panel data spanning from 1990 to 2017, the empirical results show a positive but insignificant effect of trade flows on environmental degradation. The results further show that while renewable energy dampens environmental degradation, non‐renewable energy exerts upward pressure on environmental degradation. In addition, the results provide evidence in support of a U‐shaped EKC in the long run. The study, therefore, recommends the expansion of renewable energy consumption to ensure not only environmental sustainability but also to attain the regional goal of sustainable development.
China has committed to peaking its CO2 emissions by 2030 in order to achieve its 2060 carbon neutrality target. Heavy-duty trucks (HDTs) are an important area to decarbonize, given the continuously rising greenhouse gas (GHG) emissions in this sector. Various low-carbon options have emerged, yet a comprehensive understanding of the extent to which these options can decarbonize HDT throughout the life cycle remains limited. Here, we adopt a life-cycle analysis to assess and compare the GHG mitigation potential ofhighly efficient diesel engines, battery-electrics, and hydrogen fuel cells for China’s class-8 HDTs in 2030. Results show that all three options could enable >38% life-cycle GHG reductions. The battery-electric option, however, requires well-established fast-charging infrastructures to maintain the freight-carrying capacity that will otherwise be compromised by larger batteries. Hydrogen fuel cells can attain 80% reduction when paired with low-carbon hydrogen. Hybrid strategies, including improving engine efficiency, decarbonizing power grids, optimizing freight logistics, and incentivizing behavioral changes, are necessary for the efficient and effective HDT decarbonization that is key to China achieving carbon neutrality by 2060.
We perform a state-specific life-cycle assessment of greenhouse gases (GHG) (CO2eq) and sulfur dioxide (SO2) emissions in India for representative passenger vehicles (two-wheelers, three-wheelers, four-wheelers, and buses) and technologies (internal combustion engine, battery electric, hybrid electric, and plug-in hybrid electric vehicles). We find that in most states, four-wheeler battery-electric vehicles (BEVs) have higher GHG and SO2 emissions than other conventional or alternative vehicles. Electrification of those vehicle classes under present conditions would not lead to emission reductions. Electrified buses and three-wheelers are the best strategies to reduce GHG emissions in many states, but they are also the worst strategy in terms of SO2 emissions. Electrified two-wheelers have lower SO2 emissions than gasoline in one state. The Indian grid would need to decrease its carbon dioxide emissions by 38-52% and SO2 emissions by 58-97% (depending on the state) for widespread vehicle electrification for sustainability purposes to make sense. If the 2030 goals for India under the Glasgow COP are met, we find that four-wheeler BEVs still have higher GHG emissions in 18 states compared to a conventional gasoline compact four wheeler, and all states will have higher SO2 emissions for BEVs across all vehicle types compared to their conventional counterparts.
During its Presidency of the Group of Twenty (G20) in 2020, Saudi Arabia launched the concept of the circular carbon economy (CCE) as a framework for reducing emissions to a level consistent with the goals of the Paris Agreement. The concept, which was endorsed by both G20 leaders and energy ministers, comes at a time when Saudi Arabia appears to have stabilised its domestic emissions after decades of rapid growth. This article describes the CCE concept and positions it alongside other related sustainability concepts, and assesses how it may be meaningfully applied. To that end, the article provides an analysis of key greenhouse gas (GHG) and carbon dioxide (CO2) trends in Saudi Arabia and existing climate change-related policies and measures using the CCE as a framing tool. Prominent data sources indicate that Saudi Arabia’s total CO2 emissions experienced their first-ever significant decline in 2018, of 3.93%. The analysis suggests that policy-induced variables, namely energy price reform and more robust energy efficiency measures, played a significant role in this. The article also explores the potential of key mitigation options if limiting global warming below 2°C is to be achieved. Finally, the article discusses the international significance of the CCE concept and its potential to foster stronger engagement on net-zero pathways, particularly from fossil-fuel rich countries and hard-to-abate sectors.
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Cellulose in particular and phytomass in general are at the heart of our food system. They are also a central energy vector and a vital source of materials. In this article, a multiscale approach to the complex issue of lignocellulose sustainability is developed. Global thermodynamic concepts help to place current biomass exploitation in a global energetic context. In particular, the notion of entropy appears pivotal to understand energy and material fluxes at the scale of the planet and the limits of biomass production. Entropy is, however, best described at the microscopic scale, despite its large-scale consequences. Recent advances in entropy-driven colloid assembly parallel nature's choices and lignocellulose assembly at the nanometric scale. The functional concept of exergy is then developed and a few examples of its concrete use in photosynthesis and biorefinery research are given. In a subsequent part, an evaluation of the relative importance of biomass is performed with respect to non-renewable materials. This discussion helps to explain the interdependence of resources, including ores and fossil fuels. This interdependence has important consequences for current and future biomass uses. Some of these dependences are then quantitatively discussed using life cycle analysis (LCA) results from the literature. These results are of importance to different technological fields such as paper, biobased insulation, construction wood, information and communication technologies, and biobased textiles. A conclusion is then drawn that exposes the research tracks that are the most likely to be sustainable, including self-assembly, exergetically favourable options and low tech solutions.
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Oil is China’s second-largest energy source, so it is essential to understand the country’s greenhouse gas emissions from crude-oil production. Chinese crude supply is sourced from numerous major global petroleum producers. Here, we use a per-barrel well-to-refinery life-cycle analysis model with data derived from hundreds of public and commercial sources to model the Chinese crude mix and the upstream carbon intensities and energetic productivity of China’s crude supply. We generate a carbon-denominated supply curve representing Chinese crude-oil supply from 146 oilfields in 20 countries. The selected fields are estimated to emit between ~1.5 and 46.9 g CO2eq MJ⁻¹ of oil, with volume-weighted average emissions of 8.4 g CO2eq MJ⁻¹. These estimates are higher than some existing databases, illustrating the importance of bottom-up models to support life-cycle analysis databases. This study provides quantitative insight into China’s energy policy and the economic and environmental implications of China’s oil consumption.
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Constrained oil supply has given way to abundance at a time when strong action on climate change is wavering. Recent innovation has pushed US oil production to all-time heights and driven oil prices lower. At the same time, attention to climate policy is wavering due to geopolitical upheaval. Nevertheless, climate-wise choices in the oil sector remain a priority, given oil's large role in modern economies. Here we use a set of open-source models along with a detailed dataset comprising 75 global crude oils (~25% of global production) to estimate the effects of carbon intensity and oil demand on decadal scale oil-sector emissions. We find that oil resources are abundant relative to all projections of 21st century demand, due to large light-tight oil (LTO) and heavy oil/bitumen (HOB) resources. We then investigate the 'barrel forward' emissions from producing, refining, and consuming all products from a barrel of crude. These oil resources have diverse life-cycle-greenhouse gas (LC-GHG) emissions impacts, and median per-barrel emissions for unconventional resources vary significantly. Median HOB life cycle emissions are 1.5 times those of median LTO emissions, exceeding them by 200 kgCO2eq./bbl. We show that reducing oil LC-GHGs is a mitigation opportunity worth 10–50 gigatonnes CO2 eq. cumulatively by 2050. We discuss means to reduce oil sector LC-GHGs. Results point to the need for policymakers to address both oil supply and oil demand when considering options to reduce LC-GHGs.
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Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community. We discuss changes compared to previous estimates and consistency within and among components, alongside methodology and data limitations. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on combined evidence from land-cover change data, fire activity associated with deforestation, and models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The mean ocean CO2 sink (SOCEAN) is based on observations from the 1990s, while the annual anomalies and trends are estimated with ocean models. The variability in SOCEAN is evaluated with data products based on surveys of ocean CO2 measurements. The global residual terrestrial CO2 sink (SLAND) is estimated by the difference of the other terms of the global carbon budget and compared to results of independent dynamic global vegetation models. We compare the mean land and ocean fluxes and their variability to estimates from three atmospheric inverse methods for three broad latitude bands. All uncertainties are reported as ±1σ, reflecting the current capacity to characterise the annual estimates of each component of the global carbon budget. For the last decade available (2006–2015), EFF was 9.3 ± 0.5 GtC yr−1, ELUC 1.0 ± 0.5 GtC yr−1, GATM 4.5 ± 0.1 GtC yr−1, SOCEAN 2.6 ± 0.5 GtC yr−1, and SLAND 3.1 ± 0.9 GtC yr−1. For year 2015 alone, the growth in EFF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr−1, showing a slowdown in growth of these emissions compared to the average growth of 1.8 % yr−1 that took place during 2006–2015. Also, for 2015, ELUC was 1.3 ± 0.5 GtC yr−1, GATM was 6.3 ± 0.2 GtC yr−1, SOCEAN was 3.0 ± 0.5 GtC yr−1, and SLAND was 1.9 ± 0.9 GtC yr−1. GATM was higher in 2015 compared to the past decade (2006–2015), reflecting a smaller SLAND for that year. The global atmospheric CO2 concentration reached 399.4 ± 0.1 ppm averaged over 2015. For 2016, preliminary data indicate the continuation of low growth in EFF with +0.2 % (range of −1.0 to +1.8 %) based on national emissions projections for China and USA, and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. In spite of the low growth of EFF in 2016, the growth rate in atmospheric CO2 concentration is expected to be relatively high because of the persistence of the smaller residual terrestrial sink (SLAND) in response to El Niño conditions of 2015–2016. From this projection of EFF and assumed constant ELUC for 2016, cumulative emissions of CO2 will reach 565 ± 55 GtC (2075 ± 205 GtCO2) for 1870–2016, about 75 % from EFF and 25 % from ELUC. This living data update documents changes in the methods and data sets used in this new carbon budget compared with previous publications of this data set (Le Quéré et al., 2015b, a, 2014, 2013). All observations presented here can be downloaded from the Carbon Dioxide Information Analysis Center.
Technical Report
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This background report for the OECD Round Table on Sustainable Development summarizes the discussion on divestment of fossil fuels and stranded assets. It also opens the question of how policy can best accompany activities in decline as climate policy becomes more ambitious.
Record-breaking temperatures have induced governments to implement targets for reducing future greenhouse gas (GHG) emissions. Use of oil products contributes ∼35% of global GHG emissions, and the oil industry itself consumes 3-4% of global primary energy. Because oil resources are becoming increasingly heterogeneous, requiring different extraction and processing methods, GHG studies should evaluate oil sources using detailed project-specific data. Unfortunately, prior oil-sector GHG analysis has largely neglected the fact that the energy intensity of producing oil can change significantly over the life of a particular oil project. Here we use decades-long time-series data from twenty-five globally significant oil fields (>1 billion barrels ultimate recovery) to model GHG emissions from oil production as a function of time. We find that volumetric oil production declines with depletion, but this depletion is accompanied by significant growth-in some cases over tenfold-in per-MJ GHG emissions. Depletion requires increased energy expenditures in drilling, oil recovery, and oil processing. Using probabilistic simulation, we derive a relationship for estimating GHG increases over time, showing an expected doubling in average emissions over 25 years. These trends have implications for long-term emissions and climate modelling, as well as for climate policy.
We examine the potential for solar energy in global oil operations, including both extraction and transport (“upstream”) and refining (“downstream”). Two open-source oil-sector GHG models are applied to a set of 83 representative global oil fields and 75 refinery crude oil streams (representing ∼25% of global production). Results from these models are used to estimate per-barrel energy intensities (power, heat), which are scaled to generate country-level demand for heat and power. Multiple solar resource quality cutoff criteria are used to determine which regions may profitably use solar. Potential solar thermal capacity ranges from 19 to 44 GWth in upstream operations, and from 21 to 95 GWth in downstream operations. Potential PV deployment ranges from 6 to 11 GWe in upstream operations and 17–91 GWe in downstream operations. The ranges above are due to both per-bbl variation in energy intensity, as well as uncertainty in solar resource quality criteria. Potential solar deployment in upstream operations would displace a much smaller fraction of upstream energy use because a large fraction of global upstream energy use is are either offshore or in high latitude regions (e.g., Russia, Canada, Central Asia).
This paper outlining the environmental impact of natural gas firstly describes the recent and increasing importance of gas in Western Europe, and its discovery in the North Sea. The effects resulting from its utilisation are also described. These include the effect on the implementation of the Clean Air Act and the changes brought about in land use pattern. In making the commodity available to the consumers, the gas is brought ashore to the reception terminal and transported by a national pipeline system which is made more efficient by the construction of compressor stations. The various stages of this immense operation are described as are their associated environmental problems. The methods by which these are kept to the minimum possible whilst operating under other constraints is also detailed. A further necessary stage of the operation is gas storage made essential because production cannot be economically made to coincide with demand. Technological advance has enabled methods of storage to be developed by which disruption is minimised. This aids the distribution to the final consumer by a complicated system eventually reaching the street mains. Thus the fuel is brought directly to the point of consumption with a minimum of environmental impact.
Existing transportation fuel cycle emissions models are either general and calculate non-specific values of greenhouse gas (GHG) emissions from crude oil production, or are not available for public review and auditing. We have developed the Oil Production Greenhouse Gas Emissions Estimator (OPGEE) to provide open-source, transparent, rigorous GHG assessments for use in scientific assessment, regulatory processes, and analysis of GHG mitigation options by producers. OPGEE uses petroleum engineering fundamentals to model emissions from oil and gas production operations. We introduce OPGEE and explain the methods and assumptions used in its construction. We run OPGEE on a small set of fictional oil fields and explore model sensitivity to selected input parameters. Results show that upstream emissions from petroleum production operations can vary from 3 gCO2/MJ to over 30 gCO2/MJ using realistic ranges of input parameters. Significant drivers of emissions variation are steam injection rates, water handling requirements, and rates of flaring of associated gas.
Shell to Exit Canadian Natural Resources for $3.3 Billion ” Bloomberg
  • K Gilblom
K. Gilblom, "Shell to Exit Canadian Natural Resources for $3.3 Billion, " Bloomberg, 7 May 2018.
Oil Production Greenhouse Gas Emissions Estimator OPGEE v2.0a User guide & technical documentation
  • H M El-Houjeiri
H. M. El-Houjeiri et al., "Oil Production Greenhouse Gas Emissions Estimator OPGEE v2.0a, User guide & technical documentation" (2017).