#risk

dredmorbius@joindiaspora.com

ProPublica's "The Secret IRS Files" and the difficulties of a wealth tax

I've seen a lot of discussion, little of it elucidating, and that from both supporters and negators of this work.

Among the few interesting observations is that unrealised wealth gains are difficult to assess (https://joindiaspora.com/posts/20937383#df8c1800ab2701399f60005056264835). This has objection has some merits, and I'd like to draw attention to it.

A tax basis should be:

  • Equitable, assessed according to ability to pay.
  • Certain, rather than arbitrary
  • Convenien to pay
  • Efficient to enact

I'm not just making these up, Adam Smith has discussion in Book V, Chapter 2, of his Wealth of Nations: https://en.wikisource.org/wiki/The_Wealth_of_Nations/Book_V/Chapter_2

Where income is at least in theory precisely denominated wealth appreciation is not transational, but is based on assumptions, notably:

  • Of asset holdings themselves.
  • Of the market value of those holdings.

Both are subject to uncertainty.

FIRE is Risk

For some time, I've come to view the "FIRE" sector of the economy --- finance, real estate, and insurance --- as having the common thread of risk. That is, each is based on the premise of assessing both the current market value and the associated uncertainty regarding that, of a portfolio --- debt and assets, real estate property holdings, insurance policies, respectively in each specific case. Whilst all economic activity embodies some degree of risk, it's in the FIRE sector that risk seems to be the principle, possibly only manageable component. Actors within the sector attempt risk management through diversification, information, modeling, prediction, outcomes management, expectations management, legislated liability or immunity, and direct management of both activities and entities engaged in the sector.

Notions of economic value are then inherently notions of risk. (Among numerous other confounding factors.)

The "but it's difficult to measure" argument has also been applied, for the record, to other forms of wealth accumulation common to high-net-worth (HNW) individuals, notably stock options. The response has been to note that such options clearly have some value, though the precise valuation may not be presently knowable. Because that value has a risk component.

The ... risk ... noted by the person raising this objection was that taxation of more assessible assets might result in a flight to even less readily assessed assets further compounding the problem.

Constructing a Wealth Tax

I see a few possible options here:

  • Time-average asset value. If there's uncertainty in the present-year value of assets, use a rolling average (e.g., 2, 3, 5, .. , years) to asses value, and tax based on that. Future-weighting asset inflation might be discouraged by progressively taxing higher rates or quantities of appreciation --- better to realise tax on five years of 10% gains rather than a single year of 62% gains.
  • Yes, asset deflation could then be applied toward tax credits. Similar logic would apply.
  • Where a range of values is given, the tax basis is assessed at the high end of the range.
  • Liquidity events trigger tax settlement, including arrearages, again at progressive rates. Keeping current and accurate is encouraged.
  • Costs in computing taxable value and tax amount are assessed to the specific taxpayer in question, or institutions holding or facilitating such asset holding or transfers.
  • A crude and old mechanism was for stamp taxes on assets of value. Implementing this in a modern age might prove difficult, but as an example , ancient Chinese paper money required stamp taxes to be recognised as legitimate, effectively a tax on holding paper wealth.
  • There are a reasonably finite number of attractive asset shelters: real estate, stocks, bonds, derivatives, collectibles, and the like. Taxation of these, either in holding or transfer, increases carrying and exchange costs. Ultimately this should reduce the asset value of these investments, and return wealth to the common weal.

I'd very much like to see any substantive discussions of the strengths, weaknesses, pitfalls, and real-world behaviour of direct wealth taxation.


Previously

#TheSecretIRSFiles #IRSFiles #SecretIRSFiles #WealthTax #inequality #value #FireSector #risk #tax #taxes #inequality #ProPublica

dredmorbius@joindiaspora.com

Systems Operations is a Risk Mitigation Practice

Having done ops for much of my professional life, one thing I've realised (largely since having stepped out of the role) is that:

  • Ops is largely about risk mitigation and management.
  • Running regular scenario drills should in fact be a large part of the role function.
  • As is updating procedures with lessons learnt from the exercises.

That is, testing what happens when some eventuality occurs and how your organisation responds to it. Where those scenarios evolve as the landscape about your evolves. E.g., ransomware and associated threats are a major concern now, though they are only one of a number of potential risks.

I am not aware of any significant or widely-known guide to systems administration and operations which takes this viewpoint. The model does address many of the frustrations I've had with the role over my own career.

Keep in mind that a specific countermeasure may only address part of a risk. E.g., backups address the "we can get our data back" problem. Backups do not address the "we cannot unpublish that which has been made public" problem. So depending on your threat model, backups alone are not a complete mitigation.

#Sysadmin #DevOps #Operations #Risk #DrillBabyDrill #ScenarioPlanning #TrainingInPractice

dredmorbius@joindiaspora.com

Remember the names Arnoud Boot, Peter Hoffmann, Luc Laeven, Lev Ratnovski, as signatories to the death of all privacy and the opening of universal surveillance in all commercial and financial dealings.

Under the remarkably anodyne title "What is Really New in Fintech" and published by the International Monetary Fund blog, these four men proposed that credit ratings be improved by "tapping various nonfinancial data: the type of browser and hardware used to access the internet, the history of online searches and purchases".

https://blogs.imf.org/2020/12/17/what-is-really-new-in-fintech/

Everything you do online, transferred to, assessed, rated, and stored permanently, one would suspect, by that most highly egalitarian and trusted of all institutions, the global financial system.

Arnoud Boot is professor of Corporate Fiance and Financial Markets at the #UniversityOfAmsterdam, in the country whose census records were used during WWII to prosecute the Holocaust on the Netherland's Jewish population. Of 107,000 deported Jews, only 5,200 survived.

Peter Hoffman is an economist working at the Financial Research Division of the #EuropeanCentralBank ( #ECB ), researching microstructure of financial markets, but apparently neither ethics nor privacy and surveillance.

Luc Laeven is Director-General of the Directorate General Research of the #ECB, and previously worked with the #IMF, #WorldBank, and #ABNAmroBank.

Lev Ratnovski is Sr. Econoist at the #IMF's research department.

These men would sell your entire personal informational history to gain a few fractions of a percent of interest income.

The banality of evil indeed.

#ArnoudBoot #PeterHoffmann #LucLaeven #LevRatnovski #Surveillance #SurveillanceCapitalism #Privacy #BrowserHistory #SearchHistory #Profiling #Credit #Risk #Holocaust

dredmorbius@joindiaspora.com

In the frame of Moralising Pathology, what went wrong at Boeing?

"Boeing’s 737 Max Is a Saga of Capitalism Gone Awry":

... What made the crashes so vexing is that it was impossible to pin the blame on one central villain. Instead, the whole company seemed to be at fault. Time and again, Boeing executives and engineers didn’t take warning signs seriously enough, opted against adopting additional precautions and made decisions for the sake of saving money or raising profits. ...

https://www.nytimes.com/2020/11/24/sunday-review/boeing-737-max.html

In this case, maybe, morality is to blame. There were individual technical, regulatory, design, and management errors, yes, but the common underlying cause is a fundamental misalignment of moral values. Of engineering excellence and passenger responsibility versus short term profits, bonuses, and above all, Milton Friedman's murderous bugbear, "shareholder value".

Or was it just short-sightedness, short-term thinking, denial, and scapegoating?

See previously: Treating systemic problems as moral failings … is why California is on fire.

#boeing #737max #MoralisingPathology #CorporateCulture #ShareholderValue #EngineeringDriven #ShortTermism #ShortSightedness #risk #trust #RegulatoryCapture #blame #denial #scapegoating #distributedResponsibility #BigProblems #ethics #morals #MiltonFriedman #JackWelch #DennisMuilenburg #HarryStonecipher #DaveCalhoun

dredmorbius@joindiaspora.com

Perrow, Normal Accidents, and complex systems determinants

From comments to a post by @Joerg Fliege, preserved for easier retrieval.

Charles Perrow's model in Normal Accidents is Interactions vs. Coupling. This seems ... overly reductionist? Simple is good, too simple is not.

Breaking down Perrow's taxonomy, dimensions or factors I might apply. Ranges are generally from "easy" to "hard" in terms of successful control:

  • Coupling flexibility: loose/tight
  • Coupling count: low/high
  • Internal complexity: low/high
  • Threshold sensitivity: high/low
  • Self-restabilisation tendency: high/low
  • Constraints/tolerances (design, manufacture, operational, maintenance, training, financial): loose/tight
  • Incident consequence: low/high
  • Scale (components, mass, distance, time, energy (kinetic/potential), information, decision): low/high (absolute log)
  • Decision or response cycle: long/short
  • Environmental uniformity: high/low
  • Environmental stability: high/low
  • State determinability: high/low
  • Risk determinability: high/low
  • Controls coupling: tight/loose
  • Controls response: high/low
  • Controls limits: high/low
  • Controls complexity: low/high

That's a bunch of factors, giving a complex model, but many of these are related. I see general parameters of complexity or arity, of change (itself complexity), of tolerances or constraints, of responses, of controls, of perception or sensing. These themselves are elements of a standard control or systems model.

                   update (learn)
                         ^
                         |
state -> observe -> apply model -> decide -> act (via controls)
  ^        ^  ^                                        |
  |       /    \                                       |
  |  system    environment                             |
  |                                                    |
  +----------------------------------------------------+

Coupling is how the system relates to its environment and controls. Those couplings may also be sensors or controls.

Consequence refers to result of undesired or uncontrolled states. Relates strongly to resilience or fragility.

Internal complexity, threshold sensitivity, self-stabilisation, constraints, tolerances, and scale (a form or attribute of complexity) are all aspects of the system and its model. Consequence is a component of risk.

Decision cycle --- how rapidly responses must be made to ensure desired or controlled function --- is its own element.

Environmental uniformity and stability are exogenous factors.

State and risk determinability apply to observation and model, respectively. State is overt or manifest, risk is covert or latent. State is inherently more apparent than risk.

The controls aspects all all relate to how intuitive, responsive, limited, and complex control is. Controls mapping directly to desired outcome decrease complexity. Controls providing precise and immediate response likewise. High limits (large allowed inputs) increase control, low limits decrease it and require greater planning or more limited environments. Complexity ... my need some further refinement. Degrees of control mapping to freedoms of movement of the controlled system are useful, but complexity of interactions or in specifying inputs generally adds complexity.

On scale, I added the note "absolute log". That recognises that it's not simple large or small that is complex, but departure from familiar or equilibrium norms. A model isn't a smaller representation but a simplified one -- we model both galaxies and atoms. Starting with some familiar scale or equilibrium state, noting the orders of magnitude above or below that of a given system along various dimensions, and taking the absolute value of that, seems a reasonable first approximation of complexity of that system in that dimension.

Reducing my factors:

  • System complexity: coupling, scale, internal complexity, stability, constraints, tolerances.
  • Environmental complexity: uniformity, stability, observability, predictability.
  • State determinability.
  • Risk determinability.
  • Model complexity, accuracy, and usefulness.
  • Decision cycle: required speed, number of decisions & actions with time.
  • Consequence: Risks. Result of undesired or uncontrolled state. These may be performance degredation, harm or damage to the system itself, loss of assets, reduced production or delivery, harm to operators, harm to third-party property, environmental degradation, epistemic harm, global sytemic risk.
  • Controls: appropriateness, completeness, precision, responsiveness, limits, complexity.

That may still be too many moving parts, but I'm having trouble reducing them.

Perhaps:

  • Complexity (state, system, environment, model, controls)
  • Determinability (state, risk, consequence, decision)
  • Risk (Or fragility, resilience, consequence?)

I'm not satisfied, but it's a start.

#complexity #CharlesPerrow #ComplexSystems #NormalAccidents #control #ControlTheory #SystemsTheory #Cybernetics #Risk #Manifestation #UnintendedConsequences #ManifestFunctions #LatentFunctions #RobertKMorton

dredmorbius@joindiaspora.com

COVID-19: Why US situation is far worse than Europe's, and why this may not be immediately evident

TL;DR: In assessing relative risk status, the future must be considered, not simply the present.

An HN thread[0] discusses whether the US or Europe are experiencing a worse Covid situation. The question contains nuances and pitfalls, though the general answer seems to be:

  • The EU's situation is generally several weeks advanced relative to the US. As with the Jan--Mar 2020 interval, situations in different regions can be generally considered as time-shifts of one another rather than distinct dynamics.
  • Instant measures of current case or death rates fail to account for built-in and likely future impacts and risks. Ignoring these is a category error, though a common one.
  • The European daily trends are slowing or reversing. US trends are accelerating. The US future looks far bleaker than the European future. This contrasts with the blinding bias of considering only immediate present measures such as daily mortality.

Covid and population here come from Worldometers.

The thread begins with Aperocky's comment asserting, correctly, that "The worst hit place right now is the ~United States of America."

Responding, esja asserts "this is not true", though doesn't clarify their redefinition of "worst hit", for another two rounds of discussion, finally settling on "deaths today".

That basis is fatally (so to speak) flawed as it entirely dismisses the facts that:

  1. Cases today translate directly to deaths in the 2--4 week future, at a best-case rate of 0.5% CFR and far more plausibly 1.5--3% CFR, based on present reported cases.[1]

  2. US new cases per capita are at least on par if not worse than Europes's.

  3. Europe's daily case rates are trending at worst flat, and are generally decreasing.

  4. US case rates are rising, at an acellerating rate.

The US today reports 158,363 new cases (7-day average), and a 3% CFR. In ~2--3 weeks, likely daily deaths will be 2,375--4,750, or 7.5--15 per million.[2]

Germany, to use esja's favoured example, reports 18,363 new cases (7-day average), and a 2% CFR. In ~2--3 weeks, likely daily deaths will be 367--550, 4.4--6.6 per million.[3]

All Europe reports ~220,000 new daily cases (16 Nov 2020, not smoothed). in ~2--3 weeks, likely daily deaths will be 3,300--6,600, 4.4--8.8 per million.[4]

In all cases, baked-in future daily US mortality rates will be roughly twice those of Europe, adjusted for population and are trending still further worse. The US 'benefits' only by having begun its annual seasonal coronavirus peak some 4--8 weeks later than Europe, with an European inflection beginning in September--October and a US inflection beginning October--November.

To provide an analogy, esja is laughing at Europe being in a ditch whilst the US is racing toward a cliff's edge. Assessments of present health or wealth must include obvious future consequences or risks. Critics of EU response entirely ignore these, and reframe the initial criterion to do so.

Such analysis suffers from presentism and risk blindness and is utterly flawed.


Adapted from HN comments to the thread linked above.


Notes:

  1. Beginning here: https://news.ycombinator.com/item?id=25113115

  2. I'm ignoring the fact that reported fatalities undercount true COVID-19 fatalities as demonstrated by overall excess deaths by about 30% per an August 2020 New York Times report and other independent studies and data. This is a largely global bias, doesn't affect inter-regional comparisons, simplifies analysis, and strengthens my argument as the case I present, bleak as it is, is less severe than the actual reality

  3. Using 1.5--3% CFR.

  4. Also using 1.5%--35 CFR, despite Germany's lower experienced CFR.

  5. Worldometers does not provide continental/regional plots or smoothed trends, though law-of-large-numbers helps somewhat. Again at 1.5--3% CFR, based on reported values, whic undercounts recoveries, experienced CFR is ~4%. Using a non-smoothed current high-point number further overstates total European future mortality relative to the US.

#covid19 #UnitedStates #europe #CriticalThinking #FlawedArguments #HackerNews #risk #worldometers

dredmorbius@joindiaspora.com

Steven Pinker's Panglossianism has long annoyed me

A key to understanding why is in the nature of technical debt, complexity traps (Joseph Tainter) or progress traps (Ronald Wright), closely related to Robert K. Merton's notions of unintended consequences and manifesst vs. latent functions.

You can consider any technology (or interventions) as having attributes along several dimensions. Two of those are impact (positive or negative) and realisation timescale (short or long).

Positive Negative
Short realisation Obviously good Obviously bad
Long realisation Unobviously good Unobviously bad

Technologies with obvious quickly-realised benefits are generally and correctly adopted, those with obvious quickly-realised harms rejected. But we'll also unwisely reject technologies whose benefits are not immediately or clearly articulable, and reject those whose harms are long-delayed or unapparent. And the pathological case is when short-term obvious advantage is paired with long-term nonevident harm.

By "clearly articulable", I'm referring to the ability at social scale to effectively and accurately convey true benefit or harm. The notion of clear articuability itself not being especially clearly articuable....

For illustration: cheesecake has obvious short-term advantage, walking on hot coals obvious harms. A diet and gym routine afford only distant benefits. Leaded gasoline, Freon, DDT, and animal wet markets have all proven long-term catastrophic consequences.

As Merton notes, the notion of latent functions is itself significant:

The discovery of latent functions represents significant increments in sociological knowledge. There is another respect in which inquiry into latent functions represents a distinctive contribution of the social scientist. It is precisely the latent functions of a practice or belief which are not common knowlege, for these are unintended and generally unrecognized social and psychological consequences. As a result, findings concerning latent functions represent a greater increment in knowledge than findings concerning manifest functions. They represent, also, greater departures from "common-sense" knowledge about social life. Inasmuch as the latent functions depart, more or less, from the avowed manifestations, the research which uncovers latent functions very often produces "paradoxical" results. The seeming paradox arises from the sharp modification of a familiar popular perception which regards a standardized practice or believe only in terms of its manifest functions by indicating some of its subsidiary or collateral latent functions. The introduction of the concept of latent function in social research leads to conclusions which show that "social life is not as simple as it first seems." For as long as people confine themselves to certain consequences (e.g., manifest consequences), it is comparatively simple for them to pass moral judgements upon the practice or belief in question.

-- Robert K. Merton, "Manifest and Latent Functions", in Social Theory Re-Wired

Emphasis in original.

In the argument between those arguing for optimism vs. pessimism, the optimists have the advantage of pointing to a current set of known good states --- facts in the present which can be clearly pointed to and demonstrated. A global catastrophic risk by definition has not yet ocurred and therefore of necessity exists in a latent state. Worse, it shares non-existence with an infinite universe of calamities, many or most of which can not or never will occur, and any accurate Cassandra has the burden of arguing why the risk she warns of is not among the unrealisable set. The side arguing for pessimism cannot point to any absolute proof or evidence, only indirect evidence such as similar past history, theory, probability distributions, and the like. To further compound matters, our psychological makeup resists treating such hypotheticals with the same respect granted manifested scenarios.

(There are some countervailing dynamics favouring pessimism biases. My sense is that on balance these are overwhelmed by optimism bias.)

The notion of technical debt gives us one tool for at least conceptualising, if not actually directly measuring, such costs. As a technical project, or technological adoption, progresses, trade-offs are made for present clear benefit at the exchange for some future and ill-defined cost. At which point a clarification of natures of specific aspects of risk is necessary. The future risk is not merely stochastic, the playing out of random variance on some well-known variable function, but unknown. We don't even know the possible values the dice may roll, or what cards are within the deck. I don't know of a risk terminology that applies here, though I'd suggest model risk as a term: the risk is that we don't yet have even a useful model for assessing possible outcomes or their probabilities, as contrasted with stochastic risk given a known probability function. And again, optimists and boosters have the advantage of pointing to demonstrable or clearly articulable benefits.

Among other factors in play are the likely value function on the one hand and global systemic interconnectedness on the other.

For some entity --- a cell, an individual, household, community, firm, organisation, nation, all of humanity --- any given intervention or technology offers some potential value return, falling to negative infinity at some origin (death or dissolution), and rising, at a diminishing rate, always (or very nearly almost) to some finite limit. Past a point, more of a thing is virtually always net negative. Which suggests that the possible positive benefit of any given technology is limited.

The development of an increasingly interdependent global human system --- economic, technical, political, social, epidemiological, and more --- means both that few effects are localised and that the system as a whole runs closer to its limits, with more constraints and fewer tolerances than ever before. This is Tainter's complexity trap: yes, the system's overall complexity affords capabilities not previously possible, but the complexity cost must be paid, the cost of efficiency is lost resilience.

Pinker ... ignores all this.


Adapted from a comment to a private share.

#StevenPinker #DrPangloss #risk #JosephTainter #RonaldWright #RobertKMerton #complexity #resilience #efficiency #ModelRisk #interdependence #optimism #pessimism #bias #manifestation #UnintendedConsequences #LatentFunctions

dredmorbius@joindiaspora.com

Tim Harford: why we fail to prepare for disasters

Financial Times, April 16, 2020

... Psychologists describe this inaction in the face of danger as normalcy bias or negative panic. In the face of catastrophe, from the destruction of Pompeii in AD79 to the September 11 2001 attacks on the World Trade Center, people have often been slow to recognise the danger and confused about how to respond. So they do nothing, until it is too late.

Part of the problem may simply be that we get our cues from others. In a famous experiment conducted in the late 1960s, the psychologists Bibb Latané and John Darley pumped smoke into a room in which their subjects were filling in a questionnaire.

...

The virus started to feel real to Europeans only when Europeans were suffering. Logically, it was always clear that the disease could strike middle-class people who enjoy skiing holidays in Italy; emotionally, we seemed unable to grasp that fact until it was too late.

...

Finally, there’s our seemingly limitless capacity for wishful thinking. In a complex world, we are surrounded by contradictory clues and differing opinions.

...

What if we’re thinking about this the wrong way? What if instead of seeing Sars as the warning for Covid-19, we should see Covid-19 itself as the warning?

Next time, will we be better prepared?

The dynamics of failed disaster preparedness and response, through the lenses of COVID-19, Hurricane Katrina, and other calamities. Failures in vision, leadership, unheeded warnings, cognitive biases, social normalising.

Harfords wonderful but all-too-brief podcast, Cautionary Tales is highly recommended. Its first episode, featuring the work of Charles Perrow, and airing immediately after Perrow's death last November, especially.

http://archive.is/v1gJ1

#covid19 #cognitiveBias #disasterResponse #risk #manifestation #disasterPreparedness #exponentialGrowth #unheededWarnings #hurricaneKatrina #NewOrleans #TimHarford

dredmorbius@joindiaspora.com

PlexodusWiki: Risks and Threat Models

First draft of an article, intent is to consider the issues, risks, and mitigations in various potential online service offerings.

  • Self-hosted.
  • Community- or volunteer-hosted, and hosting.
  • Commercial sites and services.

Both user and host / adminstrator perspectives should be addressed (they are not yet filled out).

Additional resources should be recommended.

I am looking for specific guidance on risks of self-hosting, p2p hosting, and federated hosting situations, a fairly new development and a rapidly changing landscape.

#plexodusWiki #googleplus #gplusrefugees #wiki #securiity #risk #references

https://social.antefriguserat.de/index.php/Risks_and_Threat_Models

dredmorbius@joindiaspora.com

Things we Need to Talk About: Risk

There's a bunch of people headed to other, often federated, self-hosted, or community-hosted platforms right now.

There are concerns with risks.

Threat model guidance, protections against external or insider abuse, useful user and admin capabilities, monitoring, organisations and lobby groups (EFF, EPIC, etc.), surveillance, state & non-state APTs (advanced persistent threats), institutional / personal lessons learned, would be hugely useful. Propaganda, disinformation, disruption, DDoS, network infiltration, and other attacks exist.

Any possible guidance, pointers, or resources would be tremendously appreciated.

There are those here, and in our circles, who've worked at, with, or for, site safety and security, moderation, administration, systems and network administration, business and legal operations, and more, in and around the social networking space, since it was called "Usenet" and "BBSes", or earlier.

I'm very grateful for the site operators who've stepped up, such as @Di Cleverly and @David Thiery, and others. I confess I'm not clear on who JoinDiaspora's own admin(s) are.

(I'd really like to know if there's any sort of Pod admin backchannel or network for coordinating issues, and what I strongly suspect is an existing discussion of these and other issues.)

I've put out feelers at Google+, though this is an effort I'd like to see be widespread. From previous industry experience there was relatively little cooperation and coordination on these matters, I'd really like to hope that the situation's improved on that score.

Lauren Weinstein's written a few brief concerns at G+, largely concerning MeWe and Diaspora:

#risk
#googleplus
#diaspora
#threadModels
#surveillance
#privacy
#security
#advancedPersistentThreats
#propaganda
#disinformation

chris_1968@pod.geraspora.de

#WASSERKRAFT - #HYDROPOWER

Yet another long-term study found that: #Hydropower dams negatively impact the #Hydromorphology (water flows, course, banks) of #Rivers and #Streams => That is, both Hydropower construction and changes in #water levels: result in changes in the "riparian zones" of streams and rivers & were shown to significantly impact: food webs, shading & water temperature - which, in turn, impact and change invertebrates & fish populations. In other words, DON'T build #Dams - they damage the water flow, the banks, the aquatic animals and the humans that depend on them.
And this study was in #Sweden. Imagine the damage multiplied 1,000-fold in the tropics!
LINK: Untangling multiple pressure impacts in Swedish boreal streams

How New #Dams in #Amazon Put Entire #World at #Risk
Do you really know how important the intact Amazon is for the #rainforests, #humans (also for #indigenous people...) and #animals there? Do you know what the Amazon basin means to the world and its climate?
Wie neue #Dämme am #Amazonas die ganze #Welt in #Gefahr bringen
Weisst Du eigentlich, wie wichtig der intakte Amazonas für die #Regenwälder, #Menschen (auch für Indigene...) und #Tiere dort ist? Weisst Du, was das #Amazonsbecken für die Welt und ihr Klima bedeutet?
Find out how an increase in hydropower from a series of over 400 planned dams could harm the Amazon Rainforest.
LINK: How New Dams in Amazon Put Entire World at Risk

via International Rivers: "Dams and #mining go hand-in-hand in the #Amazon...and they will take this #vital #ecosystem down if we don't stop them."
LINK: Unexamined synergies: dam building and mining go together in the Amazon

Would more or other, even better efforts (as, for example, looking at the #MRC activities) "move something", help?
"[...] That’s why there’s growing academic desire for an increased awareness of not just #hydro_politics, but #hydro_diplomacy – that while #water presents obvious potential conflict, it could also accelerate #global #cooperation. [...]"
LINK: The most important resource of the century - why hydro politics will shape the 21st century