impact prediction and charcetization

impact prediction

core compoent of EA that requires foerthout and foresight about potential implications of a prposed project

the source-pathway-recptor relathonship

prediction is about understanidng the causal chain

• source- the impact/actvity thats maybe harmful

• pathway - mechanasim by whchthe activty interacts with evnrometn

• recptor - envorma or human compent affected

environomental change

measurable differnce in an enviormental or socioeconomic prapeter over a specificed time

• change can be natural or caused by differnt actions not just the project

enviornmental effect ( impact)

specific portion of enviornmental change that can be attributed soley to the project

• the differnce btwn predicted future conditon with the project vs. future condition WITHOUT THE PROJECT

prediction indicators

• condition-based

  • direct measures of enviormental state

• stress based

  • - meaures of disturbances that lead to imapcts

cummaltive impacts

the total effect on a given VC resulting from incrmental impact of the proepsed project when added to impacts of all other past, present and foreseae future actions

death by a thousand cuts problem

projects indivudal imapct seems minor but can be the tipping pont that pushes a resource or ecosystem beyone critical threshold

explain this graph

cummaltive impacts graph

common approaches used to predict for EIA

• analogue 

• expert judgment

• modeling and extrapolation

• threshold-based ( MAELs)

• scenarios

analouge appraoches

learn from past by examining observed impacts of similar existing projects in comparable environoments 

• most common 

• using literature and studies

expert judgment approach

relying on opinons of dscilary experts, local communites and traditonal knoweldge holders

• Judgment has to be critical - they have to show their work and findings based on something concrete - show data from past sites,

modeling and extrapolation approach

create simplfied mathematical or conceptual represenations of real world systems to forecast outcomes

threshold-based MAELs

instead of predicting a specified outcome , focus shifts to managing project to ensure imapct doesnt exceed a predefind limit

• Side steps the need for PERCISE prediciton - once you reach that threshold is when mtiigation starts - just a threshold and once your reach that threshold you do something about it

scenarios approach

developing plausible, coherent stories or alternative images of the future to explore outcomes under differnt assumtions

• comes in handy when navigating tradeoffs

• what could happen, tool for exploring uncertainity

what is the purpose of scenarios

chalenge assumptions and test a proejcts resilence agains a range of potential futures

what are scenarios useful for

• long term predictions where trends are highly uncertian

• exploring induced imapcts that arent under the propoents direct control

• assessing cummlative imapcts invovling hypotehtical future devlopments

explain cone of uncertainty

possible, plausabile and probalby based on past trends, wildcards

• plausible is where 

  Where most of the serious secneario planning happens - blievveable, coheranet scenarios

what is characterizing predicted impacts for

• comparing and priotizing differnt impacts

• focusing managament and mtiigation efforts

• making a final determinaton of impact signifigance

classification critera of charectizing impacts

• order

• nature

• magnitude

• spatial extent

• frquency and duration

• reversibity

• likelihood

order of impact

the causal relationship of th eimpact to the projects action

• direct- 1st order - ex. flood for dam

  • an immdiate result of a project activity

• indirect- 2nd order - more mecury level in fish b/c of dcay of flooded OM

  • an effect that stems from direct impact

• induced (usually socioeconomic) - new buisness ipening to service mining workforce

  • effect resulting from spin off activities triggered by proect

nature of interaction

how the impact interacts with other impacts

• additive

  • the total impact is the sum of idnivudal imapcts

• synergistic

  • total impact is greater than the sum of idnivudal imapcts

  • One we want to look out for

  • Not a simple addicitivve effect, can combien and form a worse impact

• antagonistic

  • one adverse impact partically canels out another - LESS COMMON

accuracy vs precsions

accuracte prediction

• about the closness to the true value/outcome

precise prediction

• the level of exatness, youre being more detailed and seeing if it matches

magnitude

size, degree or concentraion of impact

• magnitude doesnt equal signfigance

  • small mag. impact on rare or senstive recpetor. could be highly signficant

• direction - refers to wheter change is postive, pad or neutral realtive to baseline

spatial extent

geographic area affected by the impact

• onsite imapcts - contained w/in projects physical footprint

  • enviormental changes that ocur within defined boundaries of the proejct

  • offsite impacts

    • enviormental changes that are triggered by project but ocur outside its phsycial boundaries

• in fly in fly out operations

  • onsite impat. is at the remote mine

  • but sig. offiste soical imapcts occurs in workers home communties (stress, family disruption etc.)

frequency duration and reversible

• frquency

  • how often imapct or disturbance occurs

• duration

  • length of time impact happens - short or long term

• reversibliliy

  • if affected componet can be returned to conditon near its predisturbance state after project ends or impact is permenant and orginal state cnat happen

likeihood

proabilty that predicted impact wil occur

risk

fuction that combines probabilty (likehood) of adverse event happpen and the severity of the consequences if event happens

• risk assemtn - process of idintify, anlayzing and evlauting risks 

  • helps decionmakers understand full spectrum of potential outcomes

• critical risks

  • low proablty but high conswquece event

  • imprtant to detail in report

problem of false certainty

findings with a high degree of confidence can lead to over confidence in mitigation and poor decision -making

uncertainty matrix

• location

  • wheres the uncertaintiy? - in input data, underlying assumtions or predicitive models

• level

  • how greats the uncetatiny - is it statistical ( known probabilities), scenario ( known posiblites, unknown proabilites, or system ( we dont know what we dont know)

• Nature

  • can it be reduced? is it knoweldge related ( more research will help) or due to inherent varialbity ( more rearch wont help)