A climate change scenario is a hypothetical future based on a "set of key driving forces".Scenarios explore the long-term effectiveness of mitigation and adaptation. Scenarios help to understand what the future may hold. They can show which decisions will have the most meaningful effects on mitigation and adaptation.
Closely related to climate change scenarios are pathways, which are more concrete and action-oriented. However, in the literature, the terms scenarios and pathways are often used interchangeably.
Many parameters influence climate change scenarios. Three
important parameters are the number of people (and population growth),
their economic activity and new technologies. Economic and energy models, such as World3 and POLES, quantify the effects of these parameters.
Climate change scenarios exist at a national, regional or global
scale. Countries use scenario studies in order to better understand
their decisions. This is useful when they are developing their
adaptation plans or nationally determined contributions. International goals for mitigating climate change like the Paris Agreement are based on studying these scenarios. For example, the IPCC Special Report on Global Warming of 1.5 °C was a "key scientific input" into the 2018 United Nations Climate Change Conference. Various pathways
are considered in the report, describing scenarios for mitigation of
global warming. Pathways include for example portfolios for energy
supply and carbon dioxide removal.
Terminology
Four climate change scenarios, based on 2015 data.Left: emissions pathways following the scenarios of (1) no policy, (2)
current policy, (3) meeting the governments’ announcements with constant
country decarbonization rates past 2030, and (4) meeting the
governments’ announcements with higher rates of decarbonization past
2030. Right: global temperatures, depending on the amount of greenhouse gases emitted in each of the four scenarios.
The IPCC Sixth Assessment Report defines scenario
as follows: "A plausible description of how the future may develop
based on a [...] set of assumptions about key driving forces and
relationships." A set of scenarios shows a range of possible futures.
Scenarios are not predictions. Scenarios help decision makers to understand what will be the effects of a decision.
The concept of pathways is closely related. The formal definition of pathways
is as follows: "The temporal evolution of natural and/or human systems
towards a future state. [...] Pathway approaches [...] involve various
dynamics, goals, and actors across different scales."
In other words: pathways are a roadmap which list actions that
need to be taken to make a scenario come true. Decision makers can use a
pathway to make a plan, e.g. with regards to the timing of fossil-fuel phase out or the reduction of fossil fuel subsidies.
Pathways are more concrete and action-oriented compared to
scenarios. They provide a roadmap for achieving desired climate targets.
There can be several pathways to achieve the same scenario end point in
future.
In the literature the terms scenarios and pathways are often used interchangeably. The IPCC publications on the physical science basis tend to use scenarios more, whereas the publications on mitigation tend to use modelled emission and mitigation pathways as a term.
Types
There are the following types of scenarios:
baseline scenarios
concentrations scenarios
emissions scenarios
mitigation scenarios
reference scenarios
socio economic scenarios.
A baseline scenario is used as a reference for comparison against an alternative scenario, e.g., a mitigation scenario. A wide range of quantitative projections of greenhouse gas emissions have been produced. The "SRES" scenarios are "baseline" emissions scenarios (i.e., they assume that no future efforts are made to limit emissions), and have been frequently used in the scientific literature (see Special Report on Emissions Scenarios for details).
Purpose
Climate change scenarios can be thought of as stories of possible
futures. They allow the description of factors that are difficult to
quantify, such as governance, social structures, and institutions. There
is considerable variety among scenarios, ranging from variants of
sustainable development, to the collapse of social, economic, and
environmental systems.
The
following parameters influence what the scenarios look like: future
population levels, economic activity, the structure of governance,
social values, and patterns of technological change. No strong patterns
were found in the relationship between economic activity and GHG
emissions. Economic growth was found to be compatible with increasing or
decreasing GHG emissions. In the latter case, emissions growth is
mediated by increased energy efficiency, shifts to non-fossil energy sources, and/or shifts to a post-industrial (service-based) economy.
Factors affecting the emission projections include:
Population projections: All other factors being equal, lower population projections result in lower emissions projections.
Economic development: Economic activity is a dominant driver of energy demand and thus of GHG emissions.
Energy use: Future changes in energy systems are a fundamental determinant of future GHG emissions.
Energy intensity: This is the total primary energy supply (TPES) per unit of GDP. In all of the baseline scenarios assessments, energy intensity was
projected to improve significantly over the 21st century. The
uncertainty range in projected energy intensity was large.
Carbon intensity: This is the CO2 emissions per unit of TPES. Compared with other scenarios, Fisher et al. (2007) found that the carbon intensity was more constant in scenarios where no climate policy had been assumed. The uncertainty range in projected carbon intensity was large. At the
high end of the range, some scenarios contained the projection that
energy technologies without CO2 emissions would become
competitive without climate policy. These projections were based on the
assumption of increasing fossil fuel prices and rapid technological
progress in carbon-free technologies. Scenarios with a low improvement
in carbon intensity coincided with scenarios that had a large fossil
fuel base, less resistance to coal consumption, or lower technology
development rates for fossil-free technologies.
Land-use change: Land-use change plays an important role in climate change, impacting on emissions, sequestration and albedo.
One of the dominant drivers in land-use change is food demand.
Population and economic growth are the most significant drivers of food
demand.
In producing scenarios, an important consideration is how social and economic development will progress in developing countries. If, for example, developing countries were to follow a development
pathway similar to the current industrialized countries, it could lead
to a very large increase in emissions. Emissions do not only depend on
the growth rate of the economy. Other factors include the structural changes in the production system, technological patterns in sectors such as energy, geographical distribution of human settlements and urban structures (this affects, for example, transportation requirements), consumption patterns (e.g., housing patterns, leisure activities, etc.), and trade patterns the degree of protectionism and the creation of regional trading blocks can affect availability to technology.
In the majority of studies, the following relationships were found (but are not proof of causation):
Rising GHGs: This was associated with scenarios having a growing, post-industrial economy with globalization,
mostly with low government intervention and generally high levels of
competition. Income equality declined within nations, but there was no
clear pattern in social equity or international income equality.
Falling GHGs: In some of these scenarios, GDP rose. Other scenarios showed economic activity limited at an ecologically
sustainable level. Scenarios with falling emissions had a high level of
government intervention in the economy. The majority of scenarios
showed increased social equity and income equality within and among
nations.
Predicted trends for greenhouse gas emissions are shown in different formats:
Scenarios of global greenhouse gas emissions.
If all countries achieve their current Paris Agreement pledges, average
warming by 2100 will go far beyond the target of the Paris Agreement to
keep warming "well below 2°C".
Climate change mitigation scenarios are possible futures in which global warming is reduced by deliberate actions, such as a comprehensive switch to energy sources other than fossil fuels.
These are actions that minimize emissions so atmospheric greenhouse gas
concentrations are stabilized at levels that restrict the adverse
consequences of climate change. Using these scenarios, the examination
of the impacts of different carbon prices on an economy is enabled
within the framework of different levels of global aspirations.
The Paris Agreement has the goal to keep the increase of global temperature below 2°C, preferably below 1.5°C above pre-industrial levels to reduce effects of climate change. A typical mitigation scenario is constructed by selecting a long-range target, such as a desired atmospheric concentration of carbon dioxide (CO2), and then fitting the actions to the target, for example by placing a cap on net global and national emissions of greenhouse gases.
Concentration scenarios
This figure depicts the rates at which global CO2
emissions must decline after 2024 to limit the global temperature
increase to 1.5, 1.7, or 2.0 degrees Celsius without relying on
net-negative emissions.
Contributions to climate change, whether they cool or warm the Earth, are often described in terms of the radiative forcing or imbalance they introduce to the planet's energy budget. Now and in the future, anthropogenic
carbon dioxide is believed to be the major component of this forcing,
and the contribution of other components is often quantified in terms of
"parts-per-million carbon dioxide equivalent" (ppmCO2e), or the increment/decrement in carbon dioxide concentrations which would create a radiative forcing of the same magnitude.
450 ppm
The BLUE scenarios in the IEA's Energy Technology Perspectives publication of 2008 describe pathways to a long-range concentration of 450 ppm. Joseph Romm has sketched how to achieve this target through the application of 14 wedges.
World Energy Outlook 2008, mentioned above, also describes a "450 Policy Scenario", in which extra energy investments to 2030 amount to $9.3 trillion over the Reference Scenario. The scenario also features, after 2020, the participation of major economies such as China and India in a global cap-and-trade scheme initially operating in OECD and European Union countries. Also the less conservative 450 ppm scenario calls for extensive deployment of negative emissions, i.e. the removal of CO2 from the atmosphere. According to the International Energy Agency (IEA) and OECD, "Achieving lower concentration targets (450 ppm) depends significantly on the use of BECCS".
550 ppm
This is the target advocated (as an upper bound) in the Stern Review. As approximately a doubling of CO2 levels relative to preindustrial times, it implies a temperature increase of about three degrees, according to conventional estimates of climate sensitivity. Pacala and Socolow list 15 "wedges", any 7 of which in combination should suffice to keep CO2 levels below 550 ppm.
The International Energy Agency's World Energy Outlook
report for 2008 describes a "Reference Scenario" for the world's energy
future "which assumes no new government policies beyond those already
adopted by mid-2008", and then a "550 Policy Scenario" in which further
policies are adopted, a mixture of "cap-and-trade systems, sectoral
agreements and national measures". In the Reference Scenario, between
2006 and 2030 the world invests $26.3 trillion in energy-supply
infrastructure; in the 550 Policy Scenario, a further $4.1 trillion is
spent in this period, mostly on efficiency increases which deliver fuel cost savings of over $7 trillion.
Commonly used pathway descriptions
Closely related to climate change scenarios are pathways, which are more concrete and action-oriented.
The IPCC assessment reports talk about the following types of pathways:
Global mean near-surface air temperature and thermosteric sea-level rise anomalies relative to the 2000–2019 mean for RCP (Representative Concentration Pathway) climate change scenarios
Different RCP scenarios result in different predicted greenhouse gas
concentrations in the atmosphere (from 2000 to 2100). RCP8.5 would
result in the highest greenhouse gas concentration (measured as CO2-equivalents).
Representative Concentration Pathways (RCP) are climate change scenarios to project future greenhouse gas concentrations. These pathways (or trajectories) describe future greenhouse gas concentrations (not emissions) and have been formally adopted by the IPCC.
The pathways describe different climate change scenarios, all of which
were considered possible depending on the amount of greenhouse gases
(GHG) emitted in the years to come. The four RCPs – originally RCP2.6,
RCP4.5, RCP6, and RCP8.5 – are labelled after the expected changes in radiative forcing values from the year 1750 to the year 2100 (2.6, 4.5, 6, and 8.5 W/m2, respectively). The IPCC Fifth Assessment Report (AR5) began to use these four pathways for climate modeling and research in 2014. The higher values mean higher greenhouse gas emissions and therefore higher global surface temperatures and more pronounced effects of climate change. The lower RCP values, on the other hand, are more desirable for humans but would require more stringent climate change mitigation efforts to achieve them.
In the IPCC's Sixth Assessment Report the original pathways are now being considered together with Shared Socioeconomic Pathways. There are three new RCPs, namely RCP1.9, RCP3.4 and RCP7. A short description of the RCPs is as follows: RCP 1.9 is a pathway that limits global warming to below 1.5 °C, the aspirational goal of the Paris Agreement. RCP 2.6 is a very stringent pathway. RCP 3.4 represents an intermediate pathway between the very stringent RCP2.6 and less stringent mitigation efforts associated with RCP4.5. RCP 4.5 is described by the IPCC as an intermediate scenario. In RCP 6, emissions peak around 2080, then decline. RCP7 is a baseline outcome rather than a mitigation target. In RCP 8.5 emissions continue to rise throughout the 21st century.
For the extended RCP2.6 scenario, global warming of 0.0 to 1.2 °C
is projected for the late 23rd century (2281–2300 average), relative to
1986–2005. For the extended RCP8.5, global warming of 3.0 to 12.6 °C is projected over the same time period.
Shared Socioeconomic Pathways
Predicted
atmospheric CO₂ concentrations for different shared socioeconomic
pathways (SSPs) across the 21st century (projected by MAGICC7, a
simple/reduced complexity climate model). Each data point represents an
average of simulated values generated from five integrated assessment models.
Shared Socioeconomic Pathways (SSPs) are climate change scenarios of projected socioeconomic global changes up to 2100 as defined in the IPCC Sixth Assessment Report on climate change in 2021. They are used to derive greenhouse gas emissions scenarios with different climate policies. The SSPs provide narratives describing alternative socio-economic developments. These storylines are a qualitative description of logic relating elements of the narratives to each other. In terms of quantitative elements, they provide data accompanying the
scenarios on national population, urbanization and GDP (per capita). The SSPs can be quantified with various Integrated Assessment Models (IAMs) to explore possible future pathways both with regard to socioeconomic and climate pathways.
SSP5: Fossil-fueled Development ("Taking the Highway")
There are also ongoing efforts to downscaling European shared
socioeconomic pathways (SSPs) for agricultural and food systems,
combined with representative concentration pathways (RCP) to regionally specific, alternative socioeconomic and climate scenarios.
National climate (change) projections
To explore a wide range of plausible climatic outcomes and to enhance
confidence in the projections, national climate change projections are
often generated from multiple general circulation models (GCMs). Such climate ensembles can take the form of perturbed physics ensembles (PPE), multi-model ensembles (MME), or initial condition ensembles (ICE). As the spatial resolution of the underlying GCMs is typically quite coarse, the projections are often downscaled,
either dynamically using regional climate models (RCMs), or
statistically. Some projections include data from areas which are larger
than the national boundaries, e.g. to more fully evaluate catchment areas of transboundary rivers.
Various countries have produced their national climate projections with feedback and/or interaction with stakeholders. Such engagement efforts have helped tailoring the climate information
to the stakeholders' needs, including the provision of sector-specific
climate indicators such as degree-heating days.
Over 30 countries have reported national climate projections / scenarios in their most recent submissions to the United Nations Framework Convention on Climate Change. Many European governments have also funded national information portals on climate change.
For countries which lack adequate resources to develop their own climate change projections, organisations such as UNDP or FAO have sponsored development of projections and national adaptation programmes (NAPAs).
Decision processes, such as decisionmaking under deep uncertainty,
may use multiple climate scenarios to evaluate vulnerabilities and
function for actions under many different potential futures.
Feedback occurs when outputs of a system are routed back as inputs as part of a chain of cause and effect that forms a circuit or loop. The system can then be said to feed back into itself. The notion of cause-and-effect has to be handled carefully when applied to feedback systems:
Simple causal reasoning about a
feedback system is difficult because the first system influences the
second and second system influences the first, leading to a circular
argument. This makes reasoning based upon cause and effect tricky, and
it is necessary to analyze the system as a whole. As provided by
Webster, feedback in business is the transmission of evaluative or
corrective information about an action, event, or process to the
original or controlling source.
— Karl Johan Ã…ström and Richard M. Murray, Feedback Systems: An Introduction for Scientists and Engineers
History
Self-regulating mechanisms have existed since antiquity, and the idea of feedback started to enter economic theory in Britain by the 18th century, but it was not at that time recognized as a universal abstraction and so did not have a name.
The first ever known artificial feedback device was a float valve, for maintaining water at a constant level, invented in 270 BC in Alexandria, Egypt. This device illustrated the principle of feedback: a low water level
opens the valve, the rising water then provides feedback into the
system, closing the valve when the required level is reached. This then
reoccurs in a circular fashion as the water level fluctuates.
Centrifugal governors were used to regulate the distance and pressure between millstones in windmills since the 17th century. In 1788, James Watt designed his first centrifugal governor following a suggestion from his business partner Matthew Boulton, for use in the steam engines of their production. Early steam engines employed a purely reciprocating motion,
and were used for pumping water – an application that could tolerate
variations in the working speed, but the use of steam engines for other
applications called for more precise control of the speed.
In 1868, James Clerk Maxwell wrote a famous paper, "On governors", that is widely considered a classic in feedback control theory. This was a landmark paper on control theory and the mathematics of feedback.
The verb phrase to feed back, in the sense of returning to an earlier position in a mechanical process, was in use in the US by the 1860s, and in 1909, Nobel laureate Karl Ferdinand Braun used the term "feed-back" as a noun to refer to (undesired) coupling between components of an electronic circuit.
By the end of 1912, researchers using early electronic amplifiers (audions)
had discovered that deliberately coupling part of the output signal
back to the input circuit would boost the amplification (through regeneration), but would also cause the audion to howl or sing. This action of feeding back of the signal from output to input gave
rise to the use of the term "feedback" as a distinct word by 1920. Increasing the feedback produced superregeneration.
The development of cybernetics from the 1940s onwards was centred around the study of circular causal feedback mechanisms.
Over the years there has been some dispute as to the best definition of feedback. According to cybernetician Ashby
(1956), mathematicians and theorists interested in the principles of
feedback mechanisms prefer the definition of "circularity of action",
which keeps the theory simple and consistent. For those with more
practical aims, feedback should be a deliberate effect via some more
tangible connection.
[Practical experimenters] object to
the mathematician's definition, pointing out that this would force them
to say that feedback was present in the ordinary pendulum ... between
its position and its momentum—a "feedback" that, from the practical
point of view, is somewhat mystical. To this the mathematician retorts
that if feedback is to be considered present only when there is an
actual wire or nerve to represent it, then the theory becomes chaotic
and riddled with irrelevancies.
Focusing on uses in management theory, Ramaprasad (1983) defines
feedback generally as "...information about the gap between the actual
level and the reference level of a system parameter" that is used to
"alter the gap in some way". He emphasizes that the information by
itself is not feedback unless translated into action.
Maintaining a desired system performance despite disturbance using negative feedback to reduce system errorAn example of a negative feedback loop with goalsA positive feedback loop example
Positive feedback: If the signal feedback from output is in phase
with the input signal, the feedback is called positive feedback.
Negative feedback: If the signal feedback is out of phase by 180°
with respect to the input signal, the feedback is called negative
feedback.
As an example of negative feedback, the diagram might represent a cruise control
system in a car that matches a target speed such as the speed limit.
The controlled system is the car; its input includes the combined torque
from the engine and from the changing slope of the road (the
disturbance). The car's speed (status) is measured by a speedometer.
The error signal is the difference of the speed as measured by the
speedometer from the target speed (set point). The controller interprets
the speed to adjust the accelerator, commanding the fuel flow to the
engine (the effector). The resulting change in engine torque, the
feedback, combines with the torque exerted by the change of road grade
to reduce the error in speed, minimising the changing slope.
The terms "positive" and "negative" were first applied to
feedback prior to WWII. The idea of positive feedback already existed in
the 1920s when the regenerative circuit was made. Friis and Jensen (1924) described this circuit in a set of electronic amplifiers as a case where the "feed-back" action is positive in contrast to negative feed-back action, which they mentioned only in passing. Harold Stephen Black's classic 1934 paper first details the use of negative feedback in electronic amplifiers. According to Black:
Positive feed-back increases the gain of the amplifier, negative feed-back reduces it.
According to Mindell (2002) confusion in the terms arose shortly after this:
...Friis
and Jensen had made the same distinction Black used between "positive
feed-back" and "negative feed-back", based not on the sign of the
feedback itself but rather on its effect on the amplifier's gain. In
contrast, Nyquist and Bode, when they built on Black's work, referred to
negative feedback as that with the sign reversed. Black had trouble
convincing others of the utility of his invention in part because
confusion existed over basic matters of definition.
Even before these terms were being used, James Clerk Maxwell had described their concept through several kinds of "component motions" associated with the centrifugal governors used in steam engines. He distinguished those that lead to a continued increase in a disturbance or the amplitude of a wave or oscillation, from those that lead to a decrease of the same quality.
Terminology
The terms positive and negative feedback are defined in different ways within different disciplines.
the change of the gap between reference and actual values of a parameter or trait, based on whether the gap is widening (positive) or narrowing (negative).
the valence of the action or effect that alters the gap, based on whether it makes the recipient or observer happy (positive) or unhappy (negative).
The two definitions may be confusing, like when an incentive (reward)
is used to boost poor performance (narrow a gap). Referring to
definition 1, some authors use alternative terms, replacing positive and negative with self-reinforcing and self-correcting, reinforcing and balancing, discrepancy-enhancing and discrepancy-reducing or regenerative and degenerative respectively. And for definition 2, some authors promote describing the action or effect as positive and negativereinforcement or punishment rather than feedback. Yet even within a single discipline an example of feedback can be called
either positive or negative, depending on how values are measured or
referenced.
This confusion may arise because feedback can be used to provide information or motivate, and often has both a qualitative and a quantitative component. As Connellan and Zemke (1993) put it:
Quantitative feedback tells us how much and how many. Qualitative feedback tells us how good, bad or indifferent.
Limitations of negative and positive feedback
While simple systems can sometimes be described as one or the other
type, many systems with feedback loops cannot be shoehorned into either
type, and this is especially true when multiple loops are present.
When there are only two parts
joined so that each affects the other, the properties of the feedback
give important and useful information about the properties of the whole.
But when the parts rise to even as few as four, if every one affects
the other three, then twenty circuits can be traced through them; and
knowing the properties of all the twenty circuits does not give complete
information about the system.
Other types of feedback
In general, feedback systems can have many signals fed back and the
feedback loop frequently contain mixtures of positive and negative
feedback where positive and negative feedback can dominate at different
frequencies or different points in the state space of a system.
The term bipolar feedback has been coined to refer to biological
systems where positive and negative feedback systems can interact, the
output of one affecting the input of another, and vice versa.
Some systems with feedback can have very complex behaviors such as chaotic behaviors
in non-linear systems, while others have much more predictable
behaviors, such as those that are used to make and design digital
systems.
Feedback is used extensively in digital systems. For example,
binary counters and similar devices employ feedback where the current
state and inputs are used to calculate a new state which is then fed
back and clocked back into the device to update it.
Feedback can give rise to incredibly complex behaviors. The Mandelbrot set
(black) within a continuously colored environment is plotted by
repeatedly feeding back values through a simple equation and recording
the points on the imaginary plane that fail to diverge.
By using feedback properties, the behavior of a system can be altered
to meet the needs of an application; systems can be made stable,
responsive or held constant. It is shown that dynamical systems with a
feedback experience an adaptation to the edge of chaos.
Physics
Physical systems present feedback through the mutual interactions of
its parts. Feedback is also relevant for the regulation of experimental
conditions, noise reduction, and signal control. The thermodynamics of feedback-controlled systems has intrigued physicist since the Maxwell's demon, with recent advances on the consequences for entropy reduction and performance increase.
In biological systems such as organisms, ecosystems, or the biosphere,
most parameters must stay under control within a narrow range around a
certain optimal level under certain environmental conditions. The
deviation of the optimal value of the controlled parameter can result
from the changes in internal and external environments. A change of some
of the environmental conditions may also require change of that range
to change for the system to function. The value of the parameter to
maintain is recorded by a reception system and conveyed to a regulation
module via an information channel. An example of this is insulin oscillations.
Biological systems contain many types of regulatory circuits, both positive and negative. As in other contexts, positive and negative do not imply that the feedback causes good or bad
effects. A negative feedback loop is one that tends to slow down a
process, whereas the positive feedback loop tends to accelerate it. The mirror neurons are part of a social feedback system, when an observed action is "mirrored" by the brain—like a self-performed action.
Normal tissue integrity is preserved by feedback interactions
between diverse cell types mediated by adhesion molecules and secreted
molecules that act as mediators; failure of key feedback mechanisms in
cancer disrupts tissue function. In an injured or infected tissue, inflammatory mediators elicit feedback
responses in cells, which alter gene expression, and change the groups
of molecules expressed and secreted, including molecules that induce
diverse cells to cooperate and restore tissue structure and function.
This type of feedback is important because it enables coordination of
immune responses and recovery from infections and injuries. During
cancer, key elements of this feedback fail. This disrupts tissue
function and immunity.
Mechanisms of feedback were first elucidated in bacteria, where a nutrient elicits changes in some of their metabolic functions. Feedback is also central to the operations of genes and gene regulatory networks. Repressor (see Lac repressor) and activatorproteins are used to create genetic operons, which were identified by François Jacob and Jacques Monod in 1961 as feedback loops. These feedback loops may be positive (as in the case of the coupling
between a sugar molecule and the proteins that import sugar into a
bacterial cell), or negative (as is often the case in metabolic consumption).
On a larger scale, feedback can have a stabilizing effect on
animal populations even when profoundly affected by external changes,
although time lags in feedback response can give rise to predator-prey cycles.
In zymology,
feedback serves as regulation of activity of an enzyme by its direct
product(s) or downstream metabolite(s) in the metabolic pathway (see Allosteric regulation).
In psychology, the body receives a stimulus from the environment or internally that causes the release of hormones.
Release of hormones then may cause more of those hormones to be
released, causing a positive feedback loop. This cycle is also found in
certain behaviour. For example, "shame loops" occur in people who blush
easily. When they realize that they are blushing, they become even more
embarrassed, which leads to further blushing, and so on.
The climate system is characterized by strong positive and negative
feedback loops between processes that affect the state of the
atmosphere, ocean, and land. A simple example is the ice–albedo positive feedback loop whereby melting snow exposes more dark ground (of lower albedo), which in turn absorbs heat and causes more snow to melt.
Feedback is extensively used in control theory, using a variety of methods including state space (controls), full state feedback, and so forth. In the context of control theory, "feedback" is traditionally assumed to specify "negative feedback".
The most common general-purpose controller using a control-loop feedback mechanism is a proportional-integral-derivative
(PID) controller. Heuristically, the terms of a PID controller can be
interpreted as corresponding to time: the proportional term depends on
the present error, the integral term on the accumulation of past errors, and the derivative term is a prediction of future error, based on current rate of change.
In ancient times, the float valve was used to regulate the flow of water in Greek and Roman water clocks; similar float valves are used to regulate fuel in a carburettor and also used to regulate tank water level in the flush toilet.
The Dutch inventor Cornelius Drebbel
(1572–1633) built thermostats (c1620) to control the temperature of
chicken incubators and chemical furnaces. In 1745, the windmill was
improved by blacksmith Edmund Lee, who added a fantail to keep the face of the windmill pointing into the wind. In 1787, Tom Mead regulated the rotation speed of a windmill by using a centrifugal pendulum to adjust the distance between the bedstone and the runner stone (i.e., to adjust the load).
Internal combustion engines of the late 20th century employed mechanical feedback mechanisms such as the vacuum timing advance but mechanical feedback was replaced by electronic engine management systems once small, robust and powerful single-chip microcontrollers became affordable.
Electronic engineering
The simplest form of a feedback amplifier can be represented by the ideal block diagram made up of unilateral elements.
The use of feedback is widespread in the design of electronic components such as amplifiers, oscillators, and stateful logic circuit elements such as flip-flops and counters. Electronic feedback systems are also very commonly used to control mechanical, thermal and other physical processes.
If the signal is inverted on its way round the control loop, the system is said to have negative feedback; otherwise, the feedback is said to be positive. Negative feedback is often deliberately introduced to increase the stability
and accuracy of a system by correcting or reducing the influence of
unwanted changes. This scheme can fail if the input changes faster than
the system can respond to it. When this happens, the lag in arrival of
the correcting signal can result in over-correction, causing the output
to oscillate or "hunt". While often an unwanted consequence of system behaviour, this effect is used deliberately in electronic oscillators.
Electronic feedback loops are used to control the output of electronic devices, such as amplifiers. A feedback loop is created when all or some portion of the output is fed back to the input. A device is said to be operating open loop if no output feedback is being employed and closed loop if feedback is being used.
When two or more amplifiers are cross-coupled using positive feedback, complex behaviors can be created. These multivibrators are widely used and include:
astable circuits, which act as oscillators
monostable circuits, which can be pushed into a state, and will return to the stable state after some time
bistable circuits, which have two stable states that the circuit can be switched between
Negative feedback
Negative feedback occurs when the fed-back output signal has a
relative phase of 180° with respect to the input signal (upside down).
This situation is sometimes referred to as being out of phase,
but that term also is used to indicate other phase separations, as in
"90° out of phase". Negative feedback can be used to correct output
errors or to desensitize a system to unwanted fluctuations. In feedback amplifiers, this correction is generally for waveform distortion reduction or to establish a specified gain level. A general expression for the gain of a negative feedback amplifier is the asymptotic gain model.
Positive feedback
Positive feedback occurs when the fed-back signal is in phase with
the input signal. Under certain gain conditions, positive feedback
reinforces the input signal to the point where the output of the device oscillates between its maximum and minimum possible states. Positive feedback may also introduce hysteresis
into a circuit. This can cause the circuit to ignore small signals and
respond only to large ones. It is sometimes used to eliminate noise from
a digital signal. Under some circumstances, positive feedback may cause
a device to latch, i.e., to reach a condition in which the output is
locked to its maximum or minimum state. This fact is very widely used in
digital electronics to make bistable circuits for volatile storage of information.
The loud squeals that sometimes occurs in sound reinforcement, public address systems, and rock music are known as audio feedback.
If a microphone is in front of a loudspeaker that it is connected to,
sound that the microphone picks up comes out of the speaker, and is
picked up by the microphone and re-amplified. If the loop gain is sufficient, howling or squealing at the maximum power of the amplifier is possible.
Loop gain
Loop gain is the sum of the gain, expressed as a ratio or in decibels,
around a feedback loop. In a feedback loop, the output of a device,
process or plant is sampled and applied to alter the input, to better
control the output. The loop gain, along with the related concept of
loop phase shift, determines the behavior of the device, and particularly whether the output is stable, or unstable, which can result in oscillation. The importance of loop gain as a parameter for characterizing electronic feedback amplifiers was first recognized by Heinrich Barkhausen in 1921, and was developed further by Hendrik Wade Bode and Harry Nyquist at Bell Labs in the 1930s.
The input signal is applied to the amplifier with open-loop gainA and amplified. The output of the amplifier is applied to a feedback network with gain B,
and subtracted from the input to the amplifier. The loop gain is the
product of all gains in the loop. In the diagram shown, the loop gain
is the product of the gains of the amplifier and the feedback network, −AB. The minus sign is because the feedback signal is subtracted from the input.
The gains A and B, and therefore the loop gain, generally vary with the frequency of the input signal, and so are usually expressed as functions of the angular frequencyω in radians per second. It is often displayed as a graph with the horizontal axis frequency ω
and the vertical axis gain. In amplifiers, the loop gain is the
difference between the open-loop gain curve and the closed-loop gain
curve (actually, the 1/B curve) on a dB scale.
Oscillators are often characterized by the frequency of their output signal:
A low-frequency oscillator (LFO) is an electronic oscillator that generates a frequency below ≈20 Hz. This term is typically used in the field of audio synthesizers, to distinguish it from an audio frequency oscillator.
An audio oscillator produces frequencies in the audio range, about 16 Hz to 20 kHz.
An RF oscillator produces signals in the radio frequency (RF) range of about 100 kHz to 100 GHz.
Oscillators designed to produce a high-power AC output from a DC supply are usually called inverters.
There are two main types of electronic oscillator: the linear or harmonic oscillator and the nonlinear or relaxation oscillator.
A latch or a flip-flop is a circuit
that has two stable states and can be used to store state information.
They typically constructed using feedback that crosses over between two
arms of the circuit, to provide the circuit with a state. The circuit
can be made to change state by signals applied to one or more control
inputs and will have one or two outputs. It is the basic storage element
in sequential logic. Latches and flip-flops are fundamental building blocks of digital electronics systems used in computers, communications, and many other types of systems.
Latches and flip-flops are used as data storage elements. Such data storage can be used for storage of state, and such a circuit is described as sequential logic. When used in a finite-state machine,
the output and next state depend not only on its current input, but
also on its current state (and hence, previous inputs). It can also be
used for counting of pulses, and for synchronizing variably-timed input
signals to some reference timing signal.
Flip-flops can be either simple (transparent or opaque) or clocked
(synchronous or edge-triggered). Although the term flip-flop has
historically referred generically to both simple and clocked circuits,
in modern usage it is common to reserve the term flip-flop exclusively for discussing clocked circuits; the simple ones are commonly called latches.
Using this terminology, a latch is level-sensitive, whereas a
flip-flop is edge-sensitive. That is, when a latch is enabled it becomes
transparent, while a flip flop's output only changes on a single type
(positive going or negative going) of clock edge.
Software
Feedback loops provide generic mechanisms for controlling the
running, maintenance, and evolution of software and computing systems. Feedback-loops are important models in the engineering of adaptive
software, as they define the behaviour of the interactions among the
control elements over the adaptation process, to guarantee system
properties at run-time. Feedback loops and foundations of control theory
have been successfully applied to computing systems. In particular, they have been applied to the development of products such as IBM Db2 and IBM Tivoli. From a software perspective, the autonomic
(MAPE, monitor analyze plan execute) loop proposed by researchers of
IBM is another valuable contribution to the application of feedback
loops to the control of dynamic properties and the design and evolution
of autonomic software systems.
The sharing economy is a socio-economic system
whereby consumers share in the creation, production, distribution,
trade and consumption of goods, and services. These systems take a
variety of forms, often leveraging information technology and the Internet, particularly digital platforms, to facilitate the distribution, sharing and reuse of excess capacity in goods and services.
It can be facilitated by nonprofit organizations,
usually based on the concept of book-lending libraries, in which goods
and services are provided for free (or sometimes for a modest
subscription) or by commercial entities, in which a company provides a
service to customers for profit.
It relies on the will of the users to share and the overcoming of stranger danger.
It provides benefits, for example can lower the GHG emissions of products by 77%-85%.
Origins
Dariusz Jemielniak and Aleksandra Przegalinska credit Marcus Felson and Joe L. Spaeth's academic article "Community Structure and Collaborative Consumption" published in 1978 with coining the term economy of sharing.
The term "sharing economy" began to appear around the time of the Great Recession, enabling social technologies, and an increasing sense of urgency around global population growth and resource depletion. Lawrence Lessig was possibly first to use the term in 2008, though others claim the origin of the term is unknown.
Definition and related concepts
Sharing Business Model Compass (SBMC). Source: Cohen and Muñoz (2016).
There is a conceptual and semantic confusion caused by the many facets of Internet-based sharing leading to discussions regarding the boundaries and the scope of the sharing economy and regarding the definition of the sharing economy.Arun Sundararajan noted in 2016 that he is "unaware of any consensus on a definition of the sharing economy". As of 2015, according to a Pew Research Center survey, only 27% of Americans had heard of the term "sharing economy".
The term "sharing economy" is often used ambiguously and can imply different characteristics. Survey respondents who had heard of the term had divergent views on
what it meant, with many thinking it concerned "sharing" in the
traditional sense of the term. To this end, the terms “sharing economy” and “collaborative
consumption” have often been used interchangeably. Collaborative
consumption refers to the activities and behaviors that drive the
sharing economy, making the two concepts closely interrelated. A
definition published in the Journal of Consumer Behavior in 2015
emphasizes these synergies: “Collaborative consumption takes place in
organized systems or networks, in which participants conduct sharing
activities in the form of renting, lending, trading, bartering, and
swapping of goods, services, transportation solutions, space, or money.”
The sharing economy is sometimes understood exclusively as a peer-to-peer phenomenon while at times, it has been framed as a business-to-customer phenomenon. Additionally, the sharing economy can be understood to encompass
transactions with a permanent transfer of ownership of a resource, such
as a sale, while other times, transactions with a transfer of ownership are considered beyond the boundaries of the sharing economy. One definition of the sharing economy, developed to integrate existing
understandings and definitions, based on a systematic review is:
"the
sharing economy is an IT-facilitated peer-to-peer model for commercial
or non-commercial sharing of underutilized goods and service capacity
through an intermediary without transfer of ownership"
The
phenomenon has been defined from a legal perspective as "a for-profit,
triangular legal structure where two parties (Providers and Users) enter
into binding contracts for the provision of goods (partial transfer of
the property bundle of rights) or services (ad hoc or casual services)
in exchange for monetary payment through an online platform operated by a
third party (Platform Operator) with an active role in the definition
and development of the legal conditions upon which the goods and
services are provided." Under this definition, the "Sharing Economy" is a triangular legal
structure with three different legal actors: "1) a Platform Operator
which using technology provides aggregation and interactivity to create a
legal environment by setting the terms and conditions for all the
actors; (2) a User who consumes the good or service on the terms and
conditions set by the Platform Operator; and (3) a Provider who provides
a good or service also abiding by the Platform Operator's terms and
conditions."
While the term "sharing economy" is the term most often used, the
sharing economy is also referred to as the access economy, crowd-based
capitalism, collaborative economy, community-based economy, gig economy, peer economy, peer-to-peer (P2P) economy, platform economy, renting economy and on-demand economy, though at times some of those terms have been defined as separate if related topics.
The notion of "sharing economy" has often been considered an oxymoron, and a misnomer for actual commercial exchanges. Arnould and Rose proposed to replace the misleading term "sharing" with "mutuality". In an article in Harvard Business Review,
authors Giana M. Eckhardt and Fleura Bardhi argue that "sharing
economy" is a misnomer, and that the correct term for this activity is
access economy. The authors say, "When 'sharing' is market-mediated—when
a company is an intermediary between consumers who don't know each
other—it is no longer sharing at all. Rather, consumers are paying to
access someone else's goods or services." The article states that companies (such as Uber)
that understand this, and whose marketing highlights the financial
benefits to participants, are successful, while companies (such as Lyft) whose marketing highlights the social benefits of the service are less successful. According to George Ritzer, this trend towards increased consumer input in commercial exchanges refers to the notion of prosumption, which, as such, is not new. Jemielniak and Przegalinska note that the term sharing economy is often
used to discuss aspects of the society that do not predominantly relate
to the economy, and propose a broader term collaborative society for such phenomena.
The term "platform capitalism"
has been proposed by some scholars as more correct than "sharing
economy" in discussion of activities of for-profit companies like Uber
and Airbnb in the economy sector. Companies that try to focus on fairness and sharing, instead of just profit motive, are much less common, and have been contrastingly described as platform cooperatives (or cooperativist platforms vs capitalist platforms). In turn, projects like Wikipedia, which rely on unpaid labor of volunteers, can be classified as commons-based peer-production initiatives. A related dimension is concerned with whether users are focused on non-profit sharing or maximizing their own profit.
Sharing is a model that is adapting to the abundance of resource,
whereas for-profit platform capitalism is a model that persists in areas
where there is still a scarcity of resources.
Yochai Benkler, one of the earliest proponents of open source software, who studied the tragedy of the commons,
which refers to the idea that when people all act solely in our
self-interest, they deplete the shared resources they need for their own
quality of life, posited that network technology could mitigate this
issue through what he called "commons-based peer production", a concept first articulated in 2002. Benkler then extended that analysis to "shareable goods" in Sharing Nicely: On Shareable Goods and the emergence of sharing as a modality of economic production, written in 2004.
Actors of the sharing economy
There are a wide range of actors who participate in the sharing
economy. This includes individual users, for-profit enterprises, social
enterprise or cooperatives, digital platform companies, local
communities, non-profit enterprises and the public sector or the government. Individual users are the actors engaged in sharing goods and resources
through "peer-to-peer (P2P) or business-to-peer (B2P) transactions". The for-profit enterprises are those actors who are profit-seekers who
buy, sell, lend, rent or trade with the use of digital platforms as
means to collaborate with other actors. The social enterprises, sometimes referred to as cooperatives, are
mainly "motivated by social or ecological reasons" and seek to empower
actors as means of genuine sharing. Digital platforms are technology firms that facilitate the relationship
between transacting parties and make profits by charging commissions. The local communities are the players at the local level with varied
structures and sharing models where most activities are non-monetized
and often carried out to further develop the community. The non-profit
enterprises have a purpose of "advancing a mission or purpose" for a
greater cause and this is their primary motivation which is genuine
sharing of resources. In addition, the public sector or the government
can participate in the sharing economy by "using public infrastructures
to support or forge partnerships with other actors and to promote
innovative forms of sharing".
Geographer Lizzie Richardson describes the sharing economy as a paradox, since it is framed as both capitalist and an alternative to capitalism. A distinction can be made between free sharing, such as genuine
sharing, and for-profit sharing, often associated with companies such as
Uber, Airbnb, and TaskRabbit.
Commercial co-options of the 'sharing economy' encompass a wide range
of structures including mostly for-profit, and, to a lesser extent,
co-operative structures.
Green marks cities where Uber operates freely, pink where it faces restrictions or legal issues, and red where it is banned.
The usage of the term sharing by for-profit companies has been
described as "abuse" and "misuse" of the term, or more precisely, its commodification. In commercial applications, the sharing economy can be considered a marketing strategy more than an actual 'sharing economy' ethos; for example, Airbnb
has sometimes been described as a platform for individuals to 'share'
extra space in their homes, but in some cases, the space is rented, not
shared. Airbnb listings additionally are often owned by property management corporations. This has led to a number of legal challenges, with some jurisdiction ruling, for example, that ride sharing
through for-profit services like Uber de facto makes the drivers
indistinguishable from regular employees of ride sharing companies.
Size and growth
United States
According to a report by the United States Department of Commerce
in June 2016, quantitative research on the size and growth of the
sharing economy remains sparse. Growth estimates can be challenging to
evaluate due to different and sometimes unspecified definitions about
what sort of activity counts as sharing economy transactions. The report
noted a 2014 study by PricewaterhouseCoopers,
which looked at five components of the sharing economy: travel, car
sharing, finance, staffing and streaming. It found that global spending
in these sectors totaled about $15 billion in 2014, which was only about
5% of the total spending in those areas. The report also forecasted a
possible increase of "sharing economy" spending in these areas to $335
billion by 2025, which would be about 50% of the total spending in these
five areas. A 2015 PricewaterhouseCoopers study found that nearly one-fifth of American consumers partake in some type of sharing economy activity. A 2017 report by Diana Farrell and Fiona Greig suggested that at least in the US, sharing economy growth may have peaked.
Europe
A February 2018 study ordered by the European Commission and the Directorate-General for Internal Market, Industry, Entrepreneurship and SMEs
indicated the level of collaborative economy development between the
EU-28 countries across the transport, accommodation, finance and online
skills sectors. The size of the collaborative economy relative to the
total EU economy was estimated to be €26.5 billion in 2016. Some experts predict that shared economy could add between €160 and €572 billion to the EU economy in the upcoming years.
According to "The Sharing Economy in Europe" from 2022 the sharing economy is spreading rapidly and widely in
today's European societies; however, the sharing economy requires more
regulation at European level because of increasing problems related to
its functioning. The authors also suggest that sometimes the local
initiatives, especially when it comes to specific niches, are doing even
better than global corporations.
China
In China, the sharing economy doubled in 2016, reaching 3.45 trillion
yuan ($500 billion) in transaction volume, and was expected to grow by
40% per year on average over the next few years, according to the
country's State Information Center. In 2017, an estimated 700 million people used sharing economy platforms. According to a report from State Information Center of China,
in 2022 sharing economy is still growing and reached about 3.83
trillion yuan (US$555 billion). The report also includes an overview of 7
main sectors of China's sharing economy: domestic services, production
capacity, knowledge, and skills, shared transportation, shared
healthcare, co-working space, and shared accommodation.
In most sharing-economy platforms in China the user profiles connected to WeChat or Alipay
which require real name and identification, which ensures that service
abuse is minimised. This fact contributes to an increase in interest for
shared healthcare services.
Russia
According to TIARCENTER and the Russian Association of Electronic
Communications, eight key verticals of Russia's sharing economy (C2C
sales, odd jobs, car sharing, carpooling, accommodation rentals, shared
offices, crowdfunding, and goods sharing) grew 30% to 511 billion rubles
($7.8 billion) in 2018.
Japan
According to Sharing Economy Association of Japan, The market size of
the sharing economy in Japan in 2021 was 2.4 trillion yen. It is
expected to expand up to 14.2799 trillion yen in FY2030.
Overall the Japanese environment is not well suited for the
development of a sharing economy. Industries do not seek new
revolutionary solutions and some services are banned. For example, for ride-hailing services, Uber
is not very popular in Japan as the public transport is very sufficient
and the regulations ban from operating private car-sharing services and
taxi apps are much more popular. According to The Japan Times
(2024) it is possible that car-sharing services will be available in
the future, however only in certain areas when taxis are deemed in short
supply.
Economic effects
The impacts of the access economy in terms of costs, wages and employment are not easily measured and appear to be growing. Various estimates indicate that 30-40% of the U.S. workforce is
self-employed, part-time, temporary or freelancers. However, the exact
percentage of those performing short-term tasks or projects found via
technology platforms was not effectively measured as of 2015 by
government sources. In the U.S., one private industry survey placed the number of
"full-time independent workers" at 17.8 million in 2015, roughly the
same as 2014. Another survey estimated the number of workers who do at
least some freelance work at 53.7 million in 2015, roughly 34% of the
workforce and up slightly from 2014.
Economists Lawrence F. Katz and Alan B. Krueger
wrote in March 2016 that there is a trend towards more workers in
alternative (part-time or contract) work arrangements rather than
full-time; the percentage of workers in such arrangements rose from
10.1% in 2005 to 15.8% in late 2015. Katz and Krueger defined alternative work arrangements as "temporary
help agency workers, on-call workers, contract company workers, and
independent contractors or free-lancers". They also estimated that approximately 0.5% of all workers identify
customers through an online intermediary; this was consistent with two
others studies that estimated the amount at 0.4% and 0.6%.
At the individual transaction level, the removal of a higher
overhead business intermediary (say a taxi company) with a lower cost
technology platform helps reduce the cost of the transaction for the
customer while also providing an opportunity for additional suppliers to
compete for the business, further reducing costs. Consumers can then spend more on other goods and services, stimulating
demand and production in other parts of the economy. Classical economics
argues that innovation that lowers the cost of goods and services
represents a net economic benefit overall. However, like many new
technologies and business innovations, this trend is disruptive to
existing business models and presents challenges for governments and
regulators.
For example, should the companies providing the technology
platform be liable for the actions of the suppliers in their network?
Should persons in their network be treated as employees, receiving
benefits such as healthcare and retirement plans? If consumers tend to
be higher income persons while the suppliers are lower-income persons,
will the lower cost of the services (and therefore lower compensation of
the suppliers) worsen income inequality? These are among the many
questions the on-demand economy presents.
Cost management and budgeting by providers
Using a personal car to transport passengers or deliveries requires payment, or sufferance, of costs
for fees deducted by the dispatching company, fuel, wear and tear,
depreciation, interest, taxes, as well as adequate insurance. The driver
is typically not paid for driving to an area where fares might be found
in the volume necessary for high earnings, or driving to the location
of a pickup or returning from a drop-off point. Mobile apps have been written that help a driver be aware of and manage such costs has been introduced.
Effects on infrastructure
Ridesharing companies have affected traffic congestion
and Airbnb has affected housing availability. According to
transportation analyst Charles Komanoff, "Uber-caused congestion has
reduced traffic speeds in downtown Manhattan by around 8 percent".
Effects on crime and litigation
Depending on the structure of the country's legal system, companies
involved in the sharing economy may shift legal realm where cases
involving sharers is disputed. Technology (such as algorithmic controls)
which connects sharers also allows for the development of policies and
standards of service. Companies can act as 'guardians' of their customer
base by monitoring their employee's behavior. For example, Uber and
Lyft can monitor their employees' driving behavior, location, and
provide emergency assistance. Several studies have shown that In the United States, the sharing
economy restructures how legal disputes are resolved and who is
considered the victims of potential crime.
In the United States's civil law, the dispute is between two
individuals, determining which individual (if any) is the victim of the
other party. U.S. criminal law considers the actions of a criminal who
"victimizes" the state or federal law(s) by breaking said law(s). In
criminal law cases, a government court punishes the offender to make the
legal victim (the government) whole, but any civilian victim does not
necessarily receive restitution from the state. In civil law cases, it
is the direct victim party, not the state, who receives the compensatory
restitution, fees, or fines. While it is possible for both kinds of law
to apply to a case, the additional contracts created in sharing economy
agreements creates the opportunity for more cases to be classified as
civil law disputes. When the sharing economy is directly involved, the
victim is the individual rather than the state. This means the civilian
victim of a crime is more likely to receive compensation under a civil
law case in the sharing economy than in the criminal law precedent. The introduction of civil law cases has the potential to increase
victims' ability to be made whole, since the legal change shifts
incentives of consumers towards action.
Benefits
Suggested benefits of the sharing economy include:
Additional flexible job opportunities as gig workers
Freelance work entails better opportunities for employment, as well
as more flexibility for workers, since people have the ability to pick
and choose the time and place of their work. As freelance workers,
people can plan around their existing schedules and maintain multiple
jobs if needed. Evidence of the appeal to this type of work can be seen
from a 2015 survey conducted by the Freelancers Union, which showed that around 34% of the U.S. population was involved in freelance work.
Freelance work can also be beneficial for small businesses.
During their early developmental stages, many small companies can't
afford or aren't in need of full-time departments, but rather require
specialized work for a certain project or for a short period of time.
With freelance workers offering their services in the sharing economy,
firms are able to save money on long-term labor costs and increase
marginal revenue from their operations.
The sharing economy allows workers to set their own hours of
work. An Uber driver explains, "the flexibility extends far beyond the
hours you choose to work on any given week. Since you don’t have to make
any sort of commitment, you can easily take time off for the big
moments in your life as well, such as vacations, a wedding, the birth of
a child, and more." Workers are able to accept or reject additional work based on their
needs while using the commodities they already possess to make money. It
provides increased flexibility of work hours and wages for independent
contractors of the sharing economy.
Depending on their schedules and resources, workers can provide
services in more than one area with different companies. This allows
workers to relocate and continue earning income. Also, by working for
such companies, the transaction costs associated with occupational
licenses are significantly lowered. For example, in New York City, taxi
drivers must have a special driver's license and undergo training and
background checks, while Uber contractors can offer "their services for little more than a background check".
The percentage of seniors in the work force increased from 20.7%
in 2009 to 23.1% in 2015, an increase in part attributed to additional
employment as gig workers.
Transparent and open data increases innovation
A common premise is that when information about goods is shared (typically via an online marketplace), the value of those goods may increase for the business, for individuals, for the community and for society in general.
Many state, local and federal governments are engaged in open data initiatives and projects such as data.gov. The theory of open or "transparent" access to information enables
greater innovation, and makes for more efficient use of products and
services, and thus supporting resilient communities.
Reduction in unused value
Unused value refers to the time over which products, services, and talents lay idle. This idle time is wasted value that business models
and organizations that are based on sharing can potentially utilize.
The classic example is that the average car is unused 95% of the time. This wasted value can be a significant resource, and hence an
opportunity, for sharing economy car solutions. There is also
significant unused value in "wasted time", as articulated by Clay Shirky in his analysis of the power of crowds connected by information technology.Many people have unused capacity in the course of their day. With
social media and information technology, such people can donate small
slivers of time to take care of simple tasks that others need doing.
Examples of these crowdsourcing solutions include the for-profit Amazon Mechanical Turk and the non-profit Ushahidi.
Christopher Koopman, an author of a 2015 study by George Mason University
economists, said the sharing economy "allows people to take idle
capital and turn them into revenue sources". He has stated, "People are
taking spare bedroom[s], cars, tools they are not using and becoming
their own entrepreneurs."
Arun Sundararajan,
a New York University economist who studies the sharing economy, told a
congressional hearing that "this transition will have a positive impact
on economic growth and welfare, by stimulating new consumption, by
raising productivity, and by catalyzing individual innovation and
entrepreneurship".
Lower prices due to increased competition and reusing items
An independent data study conducted by Busbud in 2016 compared the average price of hotel rooms with the average price of Airbnb
listings in thirteen major cities in the United States. The research
concluded that in nine of the thirteen cities, Airbnb rates were lower
than hotel rates by an average price of $34.56. A further study conducted by Busbud compared the average hotel rate
with the average Airbnb rate in eight major European cities. The
research concluded that the Airbnb rates were lower than the hotel rates
in six of the eight cities by a factor of $72. Data from a separate study shows that with Airbnb's entry into the
market in Austin, Texas hotels were required to lower prices by 6
percent to keep up with Airbnb's lower prices.
The sharing economy lowers consumer costs via borrowing and recycling items.
Environmental benefits
The sharing economy reduces negative environmental impacts by
decreasing the amount of goods needed to be produced, cutting down on
industry pollution (such as reducing the carbon footprint and overall consumption of resources)
The sharing economy allows the reuse and repurpose of already
existing commodities. Under this business model, private owners share
the assets they already possess when not in use.
In 2019 a comprehensive study checked the effect of one sharing
platform, which facilitate the sharing of around 7,000 product and
services, on greenhouse gas emissions. It found the emissions were reduced by 77%-85%.
Access to goods without the requirement to purchase
The sharing economy provides people with access to goods who can't afford or have no interest in buying them.
Increase in quality of products and services
The sharing economy facilitates increased quality of service through
rating systems provided by companies involved in the sharing economy It also facilitates increased quality of service provided by incumbent
firms that work to keep up with sharing firms like Uber and Lyft.
Other benefits
A study in Intereconomics / The Review of European Economic Policy
noted that the sharing economy has the potential to bring many benefits
for the economy, while noting that this presupposes that the success of
sharing economy services reflects their business models rather than
'regulatory arbitrage' from avoiding the regulation that affects
traditional businesses.
Maximum benefit for sellers and buyers: Enables users to improve
living standards by eliminating the emotional, physical, and social
burdens of ownership. Without the need to maintain a large inventory,
deadweight loss is reduced, prices are kept low, all while remaining
competitive in the markets.
New jobs are created, and products bought, as people acquire items
such as cars or apartments to use in the sharing economy activities.
Criticism
Oxford Internet Institute Economic Geographer Mark Graham argued that
key parts of the sharing economy impose a new balance of power onto
workers. By bringing together workers in low- and high-income countries, gig
economy platforms that are not geographically confined can bring about a
'race to the bottom' for workers.
Relationship to job loss
New York Magazine
wrote that the sharing economy has succeeded in large part because the
real economy has been struggling. Specifically, in the magazine's view,
the sharing economy succeeds because of a depressed labor market, in
which "lots of people are trying to fill holes in their income by
monetizing their stuff and their labor in creative ways", and in many
cases, people join the sharing economy because they've recently lost a
full-time job, including a few cases where the pricing structure of the
sharing economy may have made their old jobs less profitable (e.g.
full-time taxi drivers who may have switched to Lyft or Uber).
The magazine writes that "In almost every case, what compels people to
open up their homes and cars to complete strangers is money, not
trust.... Tools that help people trust in the kindness of strangers
might be pushing hesitant sharing-economy participants over the
threshold to adoption. But what's getting them to the threshold in the
first place is a damaged economy and harmful public policy that has
forced millions of people to look to odd jobs for sustenance."
Uber's "audacious plan to replace human drivers" may increase job loss as even freelance driving will be replaced by automation.
However, in a report published in January 2017, Carl Benedikt Frey
found that while the introduction of Uber had not led to jobs being
lost, but had caused a reduction in the incomes of incumbent taxi
drivers of almost 10%. Frey found that the "sharing economy", and Uber,
in particular, has had substantial negative impacts on workers’ wages.
Some people believe the Great Recession led to the expansion of the sharing economy because job losses enhanced the desire for temporary work,
which is prevalent in the sharing economy. However, there are
disadvantages to the worker; when companies use contract-based
employment, the "advantage for a business of using such non-regular
workers is obvious: It can lower labor costs dramatically, often by 30
percent, since it is not responsible for health benefits, social
security, unemployment or injured workers' compensation, paid sick or
vacation leave and more. Contract workers, who are barred from forming
unions and have no grievance procedure, can be dismissed without
notice".
Treatment of workers as independent contractors and not employees
There is debate over the status of the workers within the sharing economy; whether they should be treated as independent contractors or employees
of the companies. This issue seems to be most relevant among sharing
economy companies such as Uber. The reason this has become such a major
issue is that the two types of workers are treated very differently.
Contract workers are not guaranteed any benefits and pay can be below
average. However, if they are employees, they are granted access to
benefits and pay is generally higher. This has been described as
"shifting liabilities and responsibilities" to the workers, while
denying them the traditional job security. It has been argued that this trend is de facto "obliterating the achievements of unions thus far in their struggle to secure basic mutual obligations in worker-employer relations".
In Uberland: How the Algorithms are Rewriting the Rules of Work, technology ethnographer
Alex Rosenblat argues that Uber's reluctance to classify its drivers as
"employees" strips them of their agency as the company's
revenue-generating workforce, resulting in lower compensation and, in
some cases, risking their safety. In particular, Rosenblat critiques Uber's ratings system, which she argues elevates passengers to the role of "middle managers" without offering drivers the chance to contest poor ratings.
Rosenblat notes that poor ratings, or any other number of unspecified
breaches of conduct, can result in an Uber driver's "deactivation", an
outcome Rosenblat likens to being fired without notice or stated cause.Prosecutors have used Uber's opaque firing policy as evidence of
illegal worker misclassification; Shannon Liss-Riordan, an attorney
leading a class action lawsuit against the company, claims that "the
ability to fire at will is an important factor in showing a company's
workers are employees, not independent contractors."
The California Public Utilities Commission
filed a case, later settled out of court, that "addresses the same
underlying issue seen in the contract worker controversy—whether the new
ways of operating in the sharing economy model should be subject to the
same regulations governing traditional businesses". Like Uber, Instacart faced similar lawsuits. In 2015, a lawsuit was
filed against Instacart alleging the company misclassified a person who
buys and delivers groceries as an independent contractor. Instacart had to eventually make all such people as part-time employees
and had to accord benefits such as health insurance to those
qualifying. This led to Instacart having thousands of employees
overnight from zero.
A 2015 article by economists at George Mason University argued
that many of the regulations circumvented by sharing economy businesses
are exclusive privileges lobbied for by interest groups. Workers and entrepreneurs not connected to the interest groups engaging in this rent-seeking
behavior are thus restricted from entry into the market. For example,
taxi unions lobbying a city government to restrict the number of cabs
allowed on the road prevents larger numbers of drivers from entering the
marketplace.
The same research finds that while access economy workers do lack the protections that exist in the traditional economy, many of them cannot actually find work in the traditional economy. In this sense, they are taking advantage of opportunities that the
traditional regulatory framework has not been able to provide for them.
As the sharing economy grows, governments at all levels are reevaluating
how to adjust their regulatory schemes to accommodate these workers.
However, a 2021 research on Uber's downfall in Turkey, which was
carried out with user-generated content from TripAdvisor comments and
YouTube videos related to Uber use in Istanbul, finds that the main
reasons for people to use Uber are that since the drivers are
independent, they tend to treat the customers in a kinder way than the
regular taxi drivers and that it's much cheaper to use Uber. Although, Turkish taxi drivers claim that Uber's operations in Turkey
are illegal because the independent drivers don't pay the operating
license fee, which is compulsory for taxi drivers to pay, to the
government. Their efforts led to the banning of Uber in Turkey by the
Turkish government in October 2019. After being unavailable for
approximately two years, Uber eventually became available again in
Turkey in January 2021.
Benefits not accrued evenly
Andrew Leonard, Evgeny Morozov, criticized the for-profit sector of the sharing economy, writing that
sharing economy businesses "extract" profits from their given sector by
"successfully [making] an end run around the existing costs of doing
business" – taxes, regulations, and insurance. Similarly, In the context
of online freelancing marketplaces, there have been worries that the
sharing economy could result in a 'race to the bottom' in terms of wages
and benefits: as millions of new workers from low-income countries come
online.
Susie Cagle
wrote that the benefits big sharing economy players might be making for
themselves are "not exactly" trickling down, and that the sharing
economy "doesn't build trust" because where it builds new connections,
it often "replicates old patterns of privileged access for some, and
denial for others". William Alden wrote that "The so-called sharing economy is supposed to
offer a new kind of capitalism, one where regular folks, enabled by
efficient online platforms, can turn their fallow assets into cash
machines ... But the reality is that these markets also tend to attract a
class of well-heeled professional operators, who outperform the
amateurs—just like the rest of the economy".
The local economic benefit of the sharing economy is offset by
its current form, which is that huge tech companies reap a great deal of
profit in many cases. For example, Uber, which is estimated to be worth
$50B as of mid-2015, takes up to 30% commission from the gross revenue of its drivers, leaving many drivers making less than minimum wage. This is reminiscent of a peak Rentier state
"which derives all or a substantial portion of its national revenues
from the rent of indigenous resources to external clients".
Other issues
Companies such as Airbnb and Uber
do not share reputation data. Individual behavior on any one platform
doesn't transfer to other platforms. This fragmentation has some
negative consequences, such as the Airbnb squatters who had previously
deceived Kickstarter users to the tune of $40,000. Sharing data between these platforms could have prevented the repeat
incident. Business Insider's view is that since the sharing economy is
in its infancy, this has been accepted. However, as the industry
matures, this will need to change.
Giana Eckhardt and Fleura Bardhi say that the access economy
promotes and prioritizes cheap fares and low costs rather than personal
relationships, which is tied to similar issues in crowdsourcing. For
example, consumers reap similar benefits from Zipcar as they would from a
hotel. In this example, the primary concern is the low cost. Because of
this, the "sharing economy" may not be about sharing but rather about
access. Giana Eckhardt and Fleura Bardhi say the "sharing" economy has
taught people to prioritize cheap and easy access over interpersonal
communication, and the value of going the extra mile for those
interactions has diminished.
Concentration of power can lead to unethical business practices. By using a software named 'Greyball', Uber was able to make it difficult for regulatory officials to use the application. Another schemes allegedly implemented by Uber includes using its
application to show 'phantom' cars nearby to consumers on the app,
implying shorter pick-up times than could actually be expected. Uber
denied the allegation.
Regulations that cover traditional taxi companies but not ridesharing companies can put taxis at a competitive disadvantage. Uber has faced criticism from taxi drivers worldwide due to the
increased competition. Uber has also been banned from several
jurisdictions due to failure to comply with licensing laws.
An umbrella sharing service named Sharing E Umbrella was started in 11 cities across China in 2017 lost almost all of the 300,000 umbrellas placed out for sharing purposes during the first few weeks.
Treatment of workers/Lack of employee benefits: Since access economy
companies rely on independent contractors, they are not offered the
same protections as that of full-time salary employees in terms of
workers comp, retirement plans, sick leave, and unemployment. This debate has caused Uber to have to remove their presence in several
locations such as Alaska. Uber stirred up a large controversy in Alaska
because if Uber drivers were considered registered taxi drivers, that
would mean they would be entitled to receiving workers' compensation
insurance. However, if they were considered independent contractors they
would not receive these same benefits. Due to all of the disputes, Uber
pulled services from Alaska. In addition, ride-share drivers’ status continues to be ambiguous when
it comes to legal matters. On New Year's Eve in 2013, an off-duty driver
for Uber killed a pedestrian while looking for a rider. Since the
driver was considered a contractor, Uber would not compensate for the
victim's family. The contract states that the service is a matching
platform and "the company does not provide transportation services, and
... has no liability for services ... provided by third parties."
Quality discrepancies: Since access economy companies rely on
independent workers, the quality of service can differ between various
individual providers on the same platform. In 2015, Steven Hill from the
New America Foundation cited his experience signing up to become a host on Airbnb
as simple as uploading a few photos to the website "and within 15
minutes my place was 'live' like an Airbnb rental. No background check,
no verifying my ID, no confirming my personal details, no questions
asked. Not even any contact with a real human from their trust and
safety team. Nothing." However, due to the reputation
model, customers are provided with a peer-reviewed rating of the
provider and are given a choice of whether to proceed with the
transaction.
Inadequate liability guarantees: Though some companies offer
liability guarantees such as Airbnb's "Host Guarantee" that promises to
pay up to 1 million in damages, it is extremely difficult to prove
fault.
Ownership and usage: The access economy blurs the difference between
ownership and usage, which allows for the abuse or neglect of items
absent policies.
Replacement of small local companies with large international tech
companies. For example, taxi companies tend to be locally owned and
operated, while Uber is California-based. Therefore, taxi company
profits tend to stay local, while some portion of access economy profits
flows out of the local community.
In order to reap the real benefits of a sharing economy and somehow
address some issues that revolve around it, there is a great need for
the government and policy-makers to create the “right enabling framework
based on a set of guiding principles” proposed by the World Economic
Forum. These principles are derived from the analysis of global
policymaking and consultation with experts. The following are the seven
principles for regulation in the sharing economy.
The first principle is creating space for innovation.
This entails that “governments need to provide an initially encouraging
environment while also building necessary infrastructure to allow for
the development of innovation hubs.”
The second principle is that sharing economy should be people centered.
This means that policies should be focused on “increasing the overall
welfare of the population” as well as “improving the quality of life.”
The third principle is taking a proactive approach. This
means that “new business models need to be brought into the mainstream
and governments need to make clear frameworks that minimize
uncertainty.”
The fourth principle is the assessment of the whole regulatory system
which means administrative burdens on exiting systems should be lifted
in order to give equal level of access to all actors in the network.
The fifth principle is the data-driven government. Since most
sharing economy relies on the use of digital platforms, data can be
easily collected, analyzed, and shared which can boost the urban
environment through public-private partnerships.
The sixth principle talks about the flexible governance where
actors should consider the nature of technology which is fast evolving.
This calls for a sustained dialogue with key stakeholders, so all
interests and rights are further protected and safeguarded.
The last principle is a shared regulation where all the players should be involved in regulatory discussions as well as in the enforcement of policy.
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